Nothing
inst/doc/ExamplesTukeytrendtest.R
In tukeytrend: Tukeys Trend Test via Multiple Marginal Models
## ----warning=FALSE, message=FALSE---------------------------------------------
library("xtable")
## ----echo=TRUE, eval=FALSE, warning=FALSE, message=FALSE---------------------
# library("devtools")
# install_github("lahothorn/SiTuR")
## ----Call, echo=FALSE, warning=FALSE, message=FALSE---------------------------
library("tukeytrend")
library("multcomp")
## ----Exam1, echo=TRUE, results='hide', warning=FALSE, message=FALSE-----------
data(litter, package="multcomp")
dl <- litter
dl$dosen <- as.numeric(as.character(dl$dose))
fitw <- lm(weight ~ dosen, data=dl)
ttw <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"))
exa1<-summary(glht(model=ttw$mmm, linfct=ttw$mlf))
## ----Exam1a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa1 <- data.frame(names(exa1$test$tstat),exa1$test$tstat, exa1$test$pvalues)
colnames(Exa1) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa1, digits=4, caption="Adjusted p-values for standard Tukey Trend Test",
label="tab:exa1"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam1c, echo=FALSE, fig.cap='Mean litter weights', fig.align='center',fig.pos="H", fig.width=4.5, fig.height=4.0----
boxplot(weight~dose, data=dl)
points(y=dl$weight, x=dl$dose, pch=16)
## ----Exam2, echo=TRUE, warning=FALSE, message=FALSE---------------------------
ttdf <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa1a <- summary(glht(model=ttdf$mmm, linfct=ttdf$mlf, df=min(ttdf$df)))
## ----Exam3, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
fitc <- lm(weight ~ dosen + gesttime+number, data=dl)
covars<-anova(fitc)
ttc<- tukeytrendfit(fitc, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa3 <- summary(glht(model=ttc$mmm, linfct=ttc$mlf))
## ----Exam3a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa3 <- data.frame(names(exa3$test$tstat),exa3$test$tstat, exa3$test$pvalues)
colnames(Exa3) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa3, digits=4, caption="Adjusted p-values for finite Tukey Trend Test- adjusted against covariates",
label="tab:exa3"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam4data, echo=FALSE, results='hide', warning=FALSE, message=FALSE------
liv <-
structure(list(Dose = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62.5, 62.5,
62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 125, 125, 125,
125, 125, 125, 125, 125, 125, 125, 250, 250, 250, 250, 250, 250,
250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 500, 500, 500,
500, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000
), BodyWt = c(337.6, 319.2, 368.7, 373.3, 314.9, 306, 326.2,
331.7, 319.7, 304.9, 321, 325.9, 342.9, 322.6, 330.6, 302.2,
325.2, 326.7, 297.1, 329.8, 350.8, 305.5, 333.3, 348.2, 359.9,
345.7, 338, 322.2, 351.2, 309.8, 338, 337.3, 338.8, 329.7, 349.2,
298.4, 332.6, 323.3, 318.3, 330.6, 306.1, 320.3, 301.1, 320.7,
326.8, 313.4, 324.8, 315.2, 320.1, 308.2, 278, 297.8, 320.9,
313.4, 291.2, 293.8, 294.4, 280.9, 316.8, 292.2), LiverWt = c(10.73,
9.89, 12.6, 13.43, 10.21, 10.64, 10.76, 11.34, 10.55, 8.79, 9.27,
10.62, 11.53, 9.68, 10.53, 10.05, 10.09, 10.84, 8.87, 11.49,
12.02, 9.42, 12.55, 11.11, 13.67, 10.94, 11.66, 11.03, 11.85,
10.24, 10.7, 10.59, 9.89, 10.57, 10.84, 9.54, 11.35, 10.58, 11.09,
9.93, 9.2, 9.52, 9.58, 9.62, 9.89, 8.18, 9.41, 9.05, 8.56, 8.96,
7.92, 9.25, 9.38, 9.23, 9.02, 9.07, 9.1, 7.9, 9.48, 8.46)), .Names = c("Dose",
"BodyWt", "LiverWt"), row.names = c(NA, 60L), class = "data.frame")
## ----Exam4, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
#data("liv", package="SiTuR")
liv$relLiv <-liv$LiverWt/liv$BodyWt
mod1<-lm(LiverWt~Dose, data=liv)
mod2<-lm(relLiv~Dose, data=liv)
mod3<-lm(LiverWt~Dose+BodyWt, data=liv)
tt1<- tukeytrendfit(mod1, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
tt2<- tukeytrendfit(mod2, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
tt3<- tukeytrendfit(mod3, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
cttC <- combtt(tt1,tt2, tt3)
Exa4 <- summary(glht(model=cttC$mmm, linfct=cttC$mlf))
## ----Exam5data, echo=FALSE, results='hide', warning=FALSE, message=FALSE------
clin <-
structure(list(Dose = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62.5, 62.5,
62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 125, 125, 125,
125, 125, 125, 125, 125, 125, 125, 250, 250, 250, 250, 250, 250,
250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 500, 500, 500,
500, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000
), BUN = c(17.3, 15, 15.7, 16.5, 16.7, 15.2, 16.2, 16.6, 17.9,
16.7, 18.4, 21.1, 17.1, 18.6, 18.4, 19.1, 22.9, 22.2, 19.8, 20.4,
20, 18, 17.9, 17.6, 16.7, 16, 17.3, 17.2, 15.6, 15.5, 19.2, 21.1,
20.7, 19.8, 20.4, 16.9, 16.6, 18.2, 16.8, 16.5, 21.9, 19.2, 22.3,
18.6, 18.1, 21, 16.3, 20, 15.2, 17.8, 23.4, 21.9, 22.9, 21.2,
24.3, 17.4, 17.4, 18.7, 20.5, 19), Creat = c(0.6, 0.6, 0.5, 0.5,
0.5, 0.6, 0.6, 0.5, 0.6, 0.5, 0.5, 0.5, 0.4, 0.5, 0.5, 0.5, 0.6,
0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5,
0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5, 0.5, 0.6, 0.5, 0.5, 0.6, 0.6,
0.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.6, 0.5), `TP ` = c(7.4, 6.9, 7.1, 7.1, 7.2, 6.6,
7, 6.8, 6.8, 6.5, 6.3, 6.2, 6.5, 6.5, 7, 6.4, 6.5, 6.7, 6.5,
6.4, 7, 7.3, 6.9, 6.8, 7, 7.3, 7, 7, 6.7, 6.3, 6.7, 6.7, 6.2,
6.6, 6.6, 6.8, 7.1, 6.8, 6.4, 6.6, 6.5, 6.9, 6.3, 6.9, 6.4, 6.6,
6.4, 7.4, 6.5, 7.4, 6.1, 6.4, 6.4, 6.8, 6.3, 5.7, 6.2, 6.1, 6.1,
6.7), `ALB ` = c(5, 4.7, 4.9, 4.9, 5, 4.6, 4.8, 4.6, 4.6, 4.5,
4.4, 4.3, 4.4, 4.3, 4.7, 4.4, 4.6, 4.7, 4.4, 4.5, 4.9, 5, 4.7,
4.8, 4.8, 5, 4.8, 4.8, 4.6, 4.4, 4.6, 4.7, 4.5, 4.6, 4.6, 4.8,
5, 4.7, 4.5, 4.5, 4.5, 4.8, 4.4, 4.8, 4.6, 4.7, 4.6, 5.1, 4.6,
5.1, 4.3, 4.4, 4.5, 4.8, 4.4, 4, 4.3, 4.2, 4.2, 4.7), SerumGlucose = c(137,
180, 164, 145, 126, 126, 124, 127, 120, 144, 138, 151, 136, 133,
121, 141, 131, 125, 124, 141, 131, 140, 168, 131, 141, 125, 126,
143, 137, 196, 125, 175, 188, 144, 157, 139, 150, 144, 158, 137,
142, 158, 187, 119, 159, 160, 146, 119, 141, 126, 173, 184, 123,
127, 168, 142, 125, 141, 146, 113), CreatKinase = c(202, 205,
188, 155, 160, 229, 107, 101, 277, 343, 240, 276, 247, 164, 144,
1135, 131, 189, 250, 330, 336, 428, 239, 265, 239, 284, 165,
203, 224, 264, 384, 220, 206, 271, 241, 362, 295, 317, 429, 233,
369, 366, 462, 409, 237, 419, 268, 344, 297, 420, 403, 336, 330,
362, 497, 444, 401, 337, 838, 370), ALT = c(100, 56, 70, 54,
64, 64, 61, 77, 57, 41, 192, 413, 171, 288, 225, 497, 590, 553,
623, 821, 214, 343, 70, 158, 195, 261, 122, 230, 295, 287, 227,
182, 215, 279, 468, 233, 284, 268, 192, 105, 189, 247, 333, 162,
166, 132, 161, 330, 227, 511, 196, 268, 297, 249, 334, 182, 118,
222, 322, 288), SDH = c(35.1, 18.7, 18.4, 19.6, 21.5, 27.2, 23.8,
24.9, 16.4, 11, 42, 97.6, 35.6, 58.2, 49.4, 104, 142.8, 137.6,
146, 198.4, 73.6, 114.4, 14.8, 40.4, 52.8, 83.6, 33.7, 58.7,
104.4, 75.2, 78.4, 62, 67.2, 84.8, 176.4, 56.6, 100, 103.6, 50,
26, 80.8, 88.8, 130.8, 43.2, 52.8, 33.2, 53.9, 119.6, 101.2,
252, 76.8, 140, 122.4, 121.6, 140, 60.9, 29.6, 99.2, 133.6, 106
), ALP = c(159, 150, 155, 161, 167, 187, 155, 204, 170, 155,
173, 193, 178, 213, 180, 200, 187, 225, 211, 204, 120, 119, 122,
133, 123, 118, 137, 135, 115, 116, 105, 129, 104, 110, 128, 108,
122, 114, 105, 101, 116, 111, 101, 109, 119, 122, 108, 104, 116,
103, 104, 109, 132, 98, 101, 98, 116, 116, 101, 92), Nuc = c(41.1,
40.7, 39.3, 43.2, 43.3, 50.1, 41.8, 44.7, 44.5, 45, 39.6, 41.8,
35.7, 34.5, 35.7, 48.5, 41.1, 42.5, 44, 42.4, 38.8, 47.2, 39.1,
43.8, 39.3, 41.2, 41.7, 43.7, 42.8, 40, 38.5, 42, 37, 40.2, 45.4,
39.2, 43.7, 42.5, 40.2, 37.1, 39.9, 39.3, 40.2, 40, 38.3, 50.1,
43.3, 44, 53.8, 55.5, 44.2, 47, 44, 45.2, 53.1, 48.5, 49.1, 55.4,
51.6, 50.9), TBA = c(20.5, 39.7, 21.9, 20, 14.2, 14.1, 20.5,
16.5, 12.3, 17, 21.5, 38.2, 29.6, 49.8, 55.4, 38.4, 58.5, 69.4,
60.2, 83.3, 42.4, 37.9, 34.8, 28.8, 62.9, 51.4, 15.9, 37.5, 32.4,
55.3, 36.7, 22.8, 53.1, 47.8, 46.7, 22.6, 36, 28.6, 33.2, 25.2,
61.9, 33.5, 56.4, 67.7, 36.7, 32.9, 25.9, 35.8, 27.8, 73.9, 50,
44.1, 51, 35.8, 40, 31.3, 21.5, 33.8, 51, 28.6), Cholesterol = c(102,
100, 106, 95, 96, 80, 96, 88, 95, 93, 126, 127, 103, 112, 122,
107, 117, 102, 98, 97, 97, 101, 99, 83, 89, 97, 97, 96, 91, 90,
87, 93, 75, 84, 97, 83, 88, 79, 85, 95, 89, 93, 86, 83, 76, 79,
73, 91, 74, 90, 83, 86, 84, 84, 81, 64, 75, 70, 75, 86), Triglyceride = c(236,
102, 144, 130, 125, 93, 235, 67, 145, 108, 116, 172, 90, 154,
95, 131, 135, 107, 86, 70, 70, 167, 67, 81, 90, 114, 108, 95,
86, 104, 90, 84, 75, 86, 102, 81, 55, 95, 72, 67, 79, 66, 74,
100, 69, 51, 37, 98, 86, 101, 49, 49, 85, 80, 29, 55, 59, 32,
79, 70), Chloride = c(99, 101, 104, 103, 99, 101, 103, 102, 103,
106, 98, 101, 101, 99, 100, 102, 101, 101, 101, 103, 94, 102,
104, 101, 100, 102, 94, 103, 101, 103, 97, 102, 103, 102, 104,
104, 103, 103, 109, 104, 101, 102, 107, 102, 104, 107, 103, 101,
104, 102, 101, 103, 102, 100, 105, 106, 103, 104, 105, 105),
Sodium = c(146, 141, 144, 147, 146, 147, 150, 148, 149, 147,
145, 145, 145, 147, 148, 146, 147, 150, 147, 146, 139, 146,
147, 147, 146, 149, 138, 148, 147, 144, 144, 145, 145, 147,
150, 148, 148, 146, 150, 148, 145, 145, 147, 149, 150, 147,
149, 147, 149, 148, 144, 146, 147, 143, 145, 149, 147, 148,
144, 150), Calc = c(12.2, 11.4, 11.4, 11.3, 11.7, 11.4, 12.1,
11.4, 11.9, 11.7, 11.6, 11.1, 11, 11.5, 11.8, 11.2, 11.3,
11.8, 11.6, 11.7, 11.9, 11.6, 11.7, 11.6, 11.4, 11.9, 11.9,
12, 11.6, 12.2, 11.5, 11.5, 10.9, 11.3, 11.4, 11.3, 11.6,
11.4, 11.9, 11.8, 11.4, 11.4, 11.4, 11.8, 11.2, 11.5, 11.6,
12, 11.5, 12.2, 11.1, 11.5, 11.2, 11.6, 11.2, 11.2, 11.3,
11.2, 11.1, 11.6), PHOS = c(4.9, 5.6, 3.8, 2.9, 3.8, 5.2,
5.3, 5.5, 4.9, 5.6, 5.9, 3.7, 3.9, 5.6, 6.3, 5.6, 5.5, 7.7,
5.6, 7.1, 5.7, 3.1, 4.6, 5.4, 3.8, 4.6, 5.5, 5.1, 5.1, 7,
4.6, 3.9, 4.3, 3.8, 3.1, 4.1, 3.4, 2.6, 4.8, 6, 6.3, 3.3,
4.8, 4.4, 3.7, 5.3, 6.8, 5, 3.9, 4.4, 7.4, 5, 5.3, 5.6, 5.5,
4.8, 5.7, 4.9, 7.1, 3.7), Potassium = c(5, 3.7, 3.6, 4.4,
4.9, 5.1, 4.9, 5.5, 5.1, 4.9, 5.3, 4.6, 5, 5.3, 5.7, 5.2,
5.1, 5.4, 5.2, 4.9, 4.6, 4.9, 4.1, 5.2, 4.9, 5.3, 4.8, 5.6,
5.2, 5.1, 4.9, 4.8, 5, 5.1, 4.5, 5, 5.2, 4.8, 4.4, 5.5, 4.9,
4.4, 4.4, 5.2, 5, 4.7, 5.2, 5, 5.4, 5.5, 4.9, 4, 5.2, 5.2,
5.4, 5, 5.3, 5.5, 4.7, 5.4)), .Names = c("Dose", "BUN", "Creat",
"TP ", "ALB ", "SerumGlucose", "CreatKinase", "ALT", "SDH", "ALP",
"Nuc", "TBA", "Cholesterol", "Triglyceride", "Chloride", "Sodium",
"Calc", "PHOS", "Potassium"), row.names = c(NA, 60L), class = "data.frame")
## ----Exam5, echo=FALSE, fig.cap="Boxplots of creatin kinase", message=FALSE, warning=FALSE, fig.pos="H", fig.height=3.5,fig.width=4.5, fig.align='center'----
#data("clin", package="SiTuR")
library("reshape2")
mclin <- na.omit(melt(clin, id="Dose"))
mclinA <-droplevels(mclin[mclin$variable=="CreatKinase", ])
boxplot(value ~ Dose, data=mclinA, ylab="Creatine Kinase")
points(y=mclinA$value, x=factor(mclinA$Dose), pch=16)
## ----Exam5a, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitG<-lm(value~Dose, data=mclinA)
library("sandwich")
ttO <- tukeytrendfit(fitG, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa5a<-summary(glht(model=ttO$mmm, linfct=ttO$mlf))
exa5b<-summary(glht(model=ttO$mmm, linfct=ttO$mlf, vcov = sandwich))
## ----Examb, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
fitG<-lm(value~Dose, data=mclinA)
library("sandwich")
ttG <- tukeytrendfit(fitG, dose="Dose", scaling=c("ari", "ord", "arilog"),
ddf="residual", d0shift=c(0.2,0.5,1, 2) )
exa5c<-summary(glht(model=ttG$mmm, linfct=ttG$mlf, vcov = sandwich))
## ----Exam6, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw6 <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "log", "arilog", "highvslow"))
Exa6 <- summary(glht(model=ttw6$mmm, linfct=ttw6$mlf, alternative="less"))
## ----Exam8, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw8 <- tukeytrendfit(fitc, dose="dosen", ddf="residual",
scaling=c("ari", "ord", "log", "arilog", "treat"),
ctype="Williams")
exa8<-summary(glht(ttw8$mmm, ttw8$mlf, alternative="less"))
## ----Exam8a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa8 <- data.frame(names(exa8$test$tstat),exa8$test$tstat, exa8$test$pvalues)
colnames(Exa8) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa8, digits=4, caption="Tukey and Williams Trend Test",
label="tab:exa8"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam9, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw9 <- tukeytrendfit(fitw, dose="dosen",
scaling=c("ari", "ord", "log", "arilog", "treat"),
ctype="Dunnett")
Exa9<-summary(glht(ttw9$mmm, ttw9$mlf, alternative="less"))
## ----Exam10, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
tt10 <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog", "treat"),
ctype="UmbrellaWilliams", ddf="residual")
Exa10<-summary(glht(model=tt10$mmm, linfct=tt10$mlf, alternative="less"))
## ----Exam10a, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
dl$dos500<-dl$dosen; dl$dos50<-dl$dosen
dl$dos500[dl$dosen==500] <-NA
dl$dos50[dl$dosen==500] <-NA
dl$dos50[dl$dos50==50] <-NA
fitall<-lm(weight ~ dosen, data=dl)
fit500 <- lm(weight ~ dos500, data=dl)
fit50 <- lm(weight ~ dos50, data=dl)
tt50<- tukeytrendfit(fit50, dose="dos50", scaling=c("ari"), ddf="residual")
tt500<- tukeytrendfit(fit500, dose="dos500", scaling=c("ari", "ord", "arilog"), ddf="residual")
ttall<- tukeytrendfit(fitall, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
combi10 <- combtt(tt50,tt500,ttall)
comptt<- summary(glht(model=combi10$mmm, linfct=combi10$mlf, alternative="less"))
## ----pooledC, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
library("SimComp")
set.seed(170549)
d1 <- ermvnorm(n=73,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d2 <- ermvnorm(n=61,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d3 <- ermvnorm(n=75,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d4 <- ermvnorm(n=77,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
dose <- as.numeric(rep(c(0, 1, 2, 4),c(73,61, 75, 77)))
IBDQ<-c(d1[,1], d2[,2], d3[,3], d4[,4])
rut<-data.frame(IBDQ,dose)
lmRU <-glm(IBDQ~dose, data=rut)
Cmat3 <-- c(-3, 1, 1, 1)
EXRU <- tukeytrendfit(lmRU, dose="dose", scaling=c("ari", "ord", "arilog", "treat"), ctype=Cmat3)
EXRRU <- summary(glht(model=EXRU$mmm, linfct=EXRU$mlf))
## ----poolC, echo=FALSE, results='asis', message=FALSE, warning=FALSE----------
Exrru <- data.frame(names(EXRRU$test$tstat),EXRRU$test$tstat, EXRRU$test$pvalues)
colnames(Exrru) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exrru, digits=4, caption="Tukey Trend Test and pooled dose vs. placebo test",
label="tab:exarru"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----weitere9a, echo=TRUE,results='asis', warning=FALSE, message=FALSE--------
verrier<-data.frame(
dose = c(5,10, 20, 40, 60),
events = c(28, 26,14,12,4),
n = c(70, 59, 90,88, 77))
lmV <-glm(cbind(events,n-events)~dose, data=verrier, family= binomial(link="logit"))
EXV <- tukeytrendfit(lmV, dose="dose", scaling=c("ari", "ord", "log"))
EXVV <- summary(glht(model=EXV$mmm, linfct=EXV$mlf))
## ----Exam11, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitLl <-lm(LiverWt~Dose, data=liv)
fitLb <-lm(BodyWt~Dose, data=liv)
ttLl <- tukeytrendfit(fitLl, dose="Dose", scaling=c("ari", "ord", "arilog"))
ttLb <- tukeytrendfit(fitLb, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttL <- combtt(ttLl, ttLb)
EXA11<-summary(glht(model=cttL$mmm, linfct=cttL$mlf))
## ----Exam11a, echo=FALSE, results='asis', message=FALSE, warning=FALSE--------
exa11 <- data.frame(names(EXA11$test$tstat), EXA11$test$tstat, EXA11$test$pvalues)
colnames(exa11) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(exa11, digits=4, caption="Bivariate Tukey Trend Test",
label="tab:ex11"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam15, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
# data("liv", package="SiTuR")
livv<-liv
livv[3,2] <- livv[14,2] <- livv[19,2] <- livv[46,2] <- livv[53,2] <- livv[54,2]<- NA
livv[1,3] <- livv[11,3] <- livv[18,3] <- livv[42,3] <- livv[55,3]<- NA #missing MCAR
fitLlv <-lm(LiverWt~Dose, data=livv)
fitLbv <-lm(BodyWt~Dose, data=livv)
ttLlv <- tukeytrendfit(fitLlv, dose="Dose", scaling=c("ari", "ord", "arilog"))
ttLbv <- tukeytrendfit(fitLbv, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttLv <- combtt(ttLlv, ttLbv)
Exa15<-summary(glht(model=cttLv$mmm, linfct=cttLv$mlf))
## ----mulBindata, echo=FALSE, results='hide', warning=FALSE, message=FALSE-----
miceF <-
structure(list(group = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3),
ID = c(230L, 236L, 208L, 231L, 244L, 229L, 234L, 210L, 201L,
205L, 228L, 207L, 246L, 209L, 219L, 202L, 217L, 235L, 227L,
203L, 212L, 213L, 222L, 232L, 247L, 248L, 204L, 206L, 211L,
214L, 215L, 216L, 218L, 220L, 221L, 223L, 224L, 225L, 226L,
233L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 245L, 249L,
250L, 251L, 263L, 268L, 279L, 267L, 289L, 280L, 275L, 277L,
296L, 276L, 253L, 284L, 272L, 298L, 261L, 285L, 278L, 291L,
290L, 264L, 281L, 292L, 295L, 270L, 297L, 256L, 255L, 265L,
299L, 252L, 283L, 254L, 266L, 274L, 257L, 258L, 259L, 260L,
262L, 269L, 271L, 273L, 282L, 286L, 287L, 288L, 293L, 294L,
300L, 344L, 335L, 342L, 314L, 312L, 303L, 308L, 315L, 318L,
320L, 313L, 334L, 339L, 302L, 307L, 323L, 310L, 305L, 306L,
338L, 331L, 309L, 319L, 321L, 337L, 326L, 348L, 322L, 330L,
346L, 332L, 304L, 301L, 311L, 347L, 349L, 316L, 317L, 324L,
325L, 327L, 328L, 329L, 333L, 336L, 340L, 341L, 343L, 345L,
350L, 396L, 397L, 386L, 354L, 385L, 352L, 356L, 384L, 369L,
361L, 381L, 351L, 390L, 377L, 376L, 367L, 353L, 363L, 370L,
394L, 366L, 362L, 383L, 365L, 364L, 357L, 392L, 382L, 388L,
389L, 393L, 371L, 358L, 398L, 355L, 374L, 379L, 373L, 399L,
380L, 391L, 395L, 378L, 360L, 368L, 400L, 359L, 375L, 372L,
387L), death = c(178, 564, 579, 610, 630, 632, 652, 663,
665, 665, 670, 673, 673, 695, 702, 706, 717, 718, 728, 731,
731, 731, 731, 731, 731, 731, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 477, 493, 516, 519, 562, 562,
567, 578, 582, 596, 604, 605, 605, 617, 620, 627, 629, 632,
633, 649, 652, 658, 661, 664, 675, 675, 687, 719, 720, 720,
721, 726, 731, 731, 731, 732, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 371, 465, 484, 489,
520, 521, 545, 582, 590, 597, 611, 619, 628, 633, 637, 641,
652, 658, 659, 659, 661, 690, 694, 694, 698, 704, 714, 717,
720, 722, 727, 728, 731, 731, 731, 731, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 379, 450,
477, 505, 505, 518, 537, 548, 577, 588, 592, 596, 601, 603,
607, 615, 624, 626, 626, 627, 634, 652, 652, 654, 659, 662,
666, 676, 676, 676, 678, 679, 680, 681, 686, 686, 686, 687,
689, 692, 705, 711, 712, 717, 717, 718, 723, 730, 731, 731
), t01 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t02 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), t03 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), t04 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t05 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), t06 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), t07 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), t08 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t09 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t11 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t12 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t13 = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t14 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t15 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0), t16 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t17 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t18 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t19 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t20 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t21 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t22 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t23 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t24 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0), t25 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1), t26 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 1, 0, 0, 1, 0, 0, 0, 0), t27 = c(0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0), t28 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0), t29 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 1), t30 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t31 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t32 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t33 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t34 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t35 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t36 = c(0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t37 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t38 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t39 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t40 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t41 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1), t42 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,
0, 0, 1), t43 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t44 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t45 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t46 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t47 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0), t48 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t49 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t50 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t51 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t52 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t53 = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t54 = c(0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t55 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t56 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t57 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t58 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t59 = c(0,
1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0), t60 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t61 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t62 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t63 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t64 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t65 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t66 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t67 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t68 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t69 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t70 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t71 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t72 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t73 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t74 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t75 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t76 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t77 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t78 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t79 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t80 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t81 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t82 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t83 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t84 = c(0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t85 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t86 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t87 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t88 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t89 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("group", "ID", "death",
"t01", "t02", "t03", "t04", "t05", "t06", "t07", "t08", "t09",
"t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18",
"t19", "t20", "t21", "t22", "t23", "t24", "t25", "t26", "t27",
"t28", "t29", "t30", "t31", "t32", "t33", "t34", "t35", "t36",
"t37", "t38", "t39", "t40", "t41", "t42", "t43", "t44", "t45",
"t46", "t47", "t48", "t49", "t50", "t51", "t52", "t53", "t54",
"t55", "t56", "t57", "t58", "t59", "t60", "t61", "t62", "t63",
"t64", "t65", "t66", "t67", "t68", "t69", "t70", "t71", "t72",
"t73", "t74", "t75", "t76", "t77", "t78", "t79", "t80", "t81",
"t82", "t83", "t84", "t85", "t86", "t87", "t88", "t89"), row.names = c(NA,
200L), class = "data.frame")
## ----mulBin, echo=TRUE,results='asis', warning=FALSE, message=FALSE-----------
# data("miceF", package="SiTuR")
miceF$dose <- miceF$group
miceF$dose[miceF$dose==0] <-0
miceF$dose[miceF$dose==1] <-37
miceF$dose[miceF$dose==2] <-75
miceF$dose[miceF$dose==3] <-150
cst <- colSums(miceF[, 4:92])
tt5 <-as.matrix(miceF[, names(cst[cst>5])])
## ----mulBinFig, echo=FALSE, fig.cap='Presence/absence of 10 tumor classes in 200 mice', fig.pos="H", fig.width=6.5, fig.height=4.5----
tt5o <- cbind(tt5, "dose"=miceF$group+2)
ys <- (1:ncol(tt5o))/ncol(tt5o)
xs <- (1:nrow(tt5o))/nrow(tt5o)
par(bty="o", mar=c(2,4,4,1))
image(z=tt5o,y=ys, x=xs,
col=c("white", "black", "lightgrey", "lightblue", "blue", "darkblue"),
xaxt="n", yaxt="n", bty="o",
ylab="tumor", xlab="")
axis(side=2, at=ys, labels=colnames(tt5o),las=2)
axis(side=3, at=tapply(xs, miceF$dose, mean), labels=unique(miceF$dose),las=1, adj=0)
mtext(side=3, line=3, "dose group")
## ----mulBinMod, echo=TRUE, results='asis', warning=FALSE, message=FALSE-------
N24i <- glm(t24 ~ dose, data=miceF, family=binomial())
N26i <- glm(t26 ~ dose, data=miceF, family=binomial())
N27i <- glm(t27 ~ dose, data=miceF, family=binomial())
N28i <- glm(t28 ~ dose, data=miceF, family=binomial())
N29i <- glm(t29 ~ dose, data=miceF, family=binomial())
N36i <- glm(t36 ~ dose, data=miceF, family=binomial())
N41i <- glm(t41 ~ dose, data=miceF, family=binomial())
N42i <- glm(t42 ~ dose, data=miceF, family=binomial())
N59i <- glm(t59 ~ dose, data=miceF, family=binomial())
N71i <- glm(t71 ~ dose, data=miceF, family=binomial())
tu24i <- tukeytrendfit(N24i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu26i <- tukeytrendfit(N26i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu27i <- tukeytrendfit(N27i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu28i <- tukeytrendfit(N28i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu29i <- tukeytrendfit(N29i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu36i <- tukeytrendfit(N36i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu41i <- tukeytrendfit(N41i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu42i <- tukeytrendfit(N42i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu59i <- tukeytrendfit(N59i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu71i <- tukeytrendfit(N71i, dose="dose", scaling=c("ari", "ord", "arilog"))
tt10 <- combtt(tu24i, tu26i, tu27i, tu28i, tu29i, tu36i, tu41i, tu42i, tu59i, tu71i)
stt10 <- summary(asglht(tt10))
## ----mulBin2, echo=FALSE, results='asis', warning=FALSE, message=FALSE--------
Tcar <- data.frame(names(stt10$test$tstat),stt10$test$tstat,stt10$test$pvalues)
colnames(Tcar)<-c("Model","Test stats", "p-value")
print(xtable(Tcar, digits=c(2,2,2,5), caption="Tukey trend test for bivariate binary",
label="tab:carc"), include.rownames=FALSE)
## ----Exam12, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitw <- lm(weight ~ dosen + gesttime, data=dl) #lm-model
ttw <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"))
fitqp <- glm(number~dosen + gesttime, data=dl, family=quasipoisson) # glm-model
ttqp <- tukeytrendfit(fitqp, dose="dosen", scaling=c("ari", "ord", "arilog"))
cttqw <- combtt(ttqp, ttw) # combine both models
Exa12<-summary(glht(model=cttqw$mmm, linfct=cttqw$mlf))
## ----pupEGData,echo=FALSE, warning=FALSE, message=FALSE-----------------------
wuleon <-
structure(list(Litter = c(60L, 60L, 60L, 60L, 60L, 60L, 60L,
61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
61L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L,
68L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 76L, 76L,
76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L,
76L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L,
77L, 77L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 85L,
85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 93L, 93L,
93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 100L, 100L, 100L,
100L, 100L, 100L, 100L, 100L, 101L, 101L, 101L, 101L, 101L, 101L,
106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L,
106L, 106L, 106L, 106L, 107L, 107L, 107L, 107L, 107L, 107L, 107L,
107L, 107L, 107L, 107L, 107L, 114L, 114L, 114L, 114L, 114L, 114L,
114L, 114L, 114L, 114L, 114L, 114L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 122L, 122L, 122L,
122L, 122L, 122L, 122L, 122L, 122L, 122L, 123L, 123L, 123L, 123L,
123L, 123L, 123L, 123L, 123L, 123L, 130L, 130L, 130L, 130L, 130L,
130L, 130L, 130L, 130L, 130L, 130L, 131L, 131L, 131L, 131L, 131L,
131L, 131L, 131L, 131L, 131L, 131L, 131L, 138L, 138L, 138L, 138L,
138L, 138L, 138L, 138L, 138L, 138L, 138L, 138L, 138L, 139L, 139L,
139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L,
139L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L,
146L, 146L, 146L, 147L, 147L, 147L, 147L, 147L, 147L, 147L, 147L,
147L, 147L, 147L, 147L, 147L, 153L, 153L, 153L, 153L, 153L, 153L,
153L, 153L, 153L, 153L, 153L, 153L, 153L, 153L, 154L, 154L, 154L,
154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L,
54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 59L, 59L, 59L,
59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 62L, 62L,
62L, 62L, 62L, 62L, 62L, 62L, 62L, 62L, 62L, 67L, 67L, 67L, 67L,
67L, 67L, 67L, 67L, 67L, 67L, 67L, 67L, 70L, 70L, 70L, 70L, 70L,
70L, 70L, 70L, 70L, 70L, 70L, 70L, 75L, 75L, 75L, 75L, 75L, 75L,
75L, 75L, 75L, 75L, 75L, 75L, 75L, 78L, 78L, 78L, 78L, 78L, 78L,
78L, 78L, 78L, 78L, 78L, 78L, 78L, 78L, 78L, 86L, 86L, 86L, 86L,
86L, 86L, 86L, 86L, 86L, 86L, 86L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L,
99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 102L, 102L,
102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L,
102L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L,
116L, 116L, 116L, 121L, 121L, 121L, 121L, 121L, 121L, 121L, 121L,
121L, 121L, 121L, 121L, 124L, 124L, 124L, 124L, 124L, 124L, 124L,
124L, 124L, 124L, 129L, 129L, 129L, 129L, 129L, 129L, 129L, 129L,
129L, 129L, 129L, 132L, 132L, 132L, 132L, 132L, 132L, 132L, 132L,
132L, 132L, 132L, 132L, 132L, 137L, 137L, 137L, 137L, 137L, 137L,
137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 140L, 140L,
140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L,
140L, 140L, 145L, 145L, 145L, 145L, 145L, 145L, 145L, 148L, 148L,
148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 152L,
152L, 152L, 152L, 152L, 152L, 152L, 155L, 155L, 155L, 155L, 155L,
155L, 155L, 155L, 155L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L,
55L, 55L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 66L,
66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 74L, 74L, 74L, 74L, 74L,
74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 79L, 82L, 82L, 82L, 82L,
82L, 82L, 82L, 82L, 82L, 82L, 82L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 87L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 95L, 95L, 95L, 95L, 95L, 95L, 95L,
95L, 95L, 95L, 95L, 95L, 95L, 98L, 98L, 98L, 98L, 98L, 98L, 98L,
98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 103L, 103L, 103L, 103L,
103L, 103L, 103L, 103L, 103L, 103L, 103L, 105L, 105L, 105L, 105L,
105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 112L, 112L, 112L,
112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 117L,
117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L,
117L, 125L, 125L, 125L, 125L, 125L, 125L, 125L, 128L, 128L, 128L,
128L, 128L, 128L, 128L, 128L, 136L, 136L, 136L, 141L, 141L, 141L,
141L, 141L, 141L, 141L, 144L, 144L, 144L, 144L, 144L, 144L, 144L,
144L, 144L, 144L, 149L, 149L, 149L, 149L, 149L, 149L, 149L, 149L,
149L, 149L, 149L, 149L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 57L,
57L, 57L, 57L, 57L, 57L, 57L, 57L, 57L, 57L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 65L, 65L, 65L, 65L, 65L,
65L, 65L, 65L, 65L, 65L, 65L, 72L, 72L, 72L, 72L, 72L, 72L, 72L,
72L, 72L, 72L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L,
80L, 80L, 80L, 80L, 81L, 81L, 81L, 81L, 81L, 81L, 81L, 89L, 89L,
89L, 89L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L,
96L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L,
104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L,
104L, 104L, 104L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L,
119L, 119L, 119L, 119L, 126L, 126L, 126L, 126L, 126L, 126L, 126L,
126L, 126L, 126L, 126L, 126L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 134L, 134L, 134L, 134L, 134L,
134L, 134L, 134L, 134L, 134L, 135L, 135L, 135L, 135L, 135L, 135L,
135L, 135L, 135L, 142L, 142L, 142L, 142L, 142L, 142L, 142L, 142L,
142L, 143L, 143L, 143L, 143L, 143L, 143L, 143L, 150L, 150L, 150L,
150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 151L, 151L,
151L, 151L, 151L, 151L, 151L, 156L, 156L, 156L, 156L, 156L, 156L,
156L, 156L, 156L, 156L, 156L, 156L), Dose = c(0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L,
3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L, 3000L
), Weight = c(0.903, 0.828, 0.953, 0.954, 1.07, 1.065, 0.959,
1.025, 1.006, 1.069, 0.825, 1.013, 0.994, 1.059, 1.041, 1.02,
1.041, 0.911, 0.988, 0.947, 0.976, 1.058, 0.95, 0.86, 1.015,
0.907, 0.856, 1.075, 0.958, 1.082, 0.916, 1.054, 0.921, 0.874,
0.999, 0.95, 1.065, 0.944, 0.949, 1.044, 1.025, 0.987, 0.879,
0.908, 1.071, 1.056, 1.084, 0.851, 0.925, 0.832, 0.979, 1.096,
0.954, 0.875, 1.057, 1.245, 1.018, 1.123, 1.03, 0.968, 0.852,
0.94, 0.905, 0.787, 0.918, 0.745, 0.907, 0.818, 0.896, 0.858,
0.935, 0.669, 0.93, 0.89, 0.983, 0.974, 1.005, 0.897, 1.11, 1.035,
0.997, 1.059, 1.067, 0.911, 1.122, 1.134, 1.117, 0.999, 1.2,
1.154, 1.077, 0.98, 0.989, 1.161, 1.101, 1.153, 1.051, 1.082,
1.062, 0.955, 1.095, 0.994, 1.093, 1.002, 1.038, 1.084, 1.026,
0.975, 1.11, 1.12, 1.17, 1.13, 1.07, 1.1, 1.02, 1.19, 1.04, 0.821,
1.07, 0.665, 1.12, 1.044, 0.933, 0.978, 0.981, 0.958, 1.005,
1.053, 1.059, 0.917, 1.079, 1.077, 1.06, 1.031, 1.006, 1.007,
0.96, 0.844, 0.833, 0.87, 0.887, 0.835, 0.716, 0.964, 0.933,
0.753, 0.803, 0.742, 0.885, 0.903, 0.815, 0.937, 0.936, 0.984,
0.835, 0.954, 0.89, 0.93, 0.924, 0.873, 1.045, 1.087, 1.021,
0.999, 0.908, 1.027, 1.041, 0.968, 0.971, 1.029, 0.923, 0.944,
0.953, 1.032, 1.045, 0.967, 1.065, 0.981, 0.964, 1.08, 1.1, 0.994,
0.949, 1.08, 1.081, 1.012, 0.856, 0.915, 0.952, 0.983, 0.872,
0.95, 0.939, 1.014, 0.907, 0.841, 0.892, 0.868, 0.837, 0.931,
0.871, 0.901, 0.987, 0.986, 0.933, 0.935, 1.033, 1.081, 0.773,
1.054, 0.946, 0.994, 0.724, 0.883, 1.047, 0.951, 0.806, 0.858,
0.815, 0.914, 0.841, 0.866, 0.792, 0.773, 0.882, 0.887, 0.847,
1.035, 0.918, 0.886, 0.923, 1.027, 0.991, 0.909, 1.061, 1.054,
1.018, 1.029, 1.1, 0.932, 0.886, 1.024, 0.93, 1.025, 1.014, 0.954,
1.081, 0.967, 0.912, 1.03, 0.871, 0.94, 0.996, 1.028, 0.794,
0.879, 1.058, 1.1, 1.121, 1.093, 1.017, 1.058, 1.09, 1.052, 1.046,
1.103, 1.026, 1.076, 1.095, 1.037, 0.987, 1.025, 0.967, 0.785,
0.861, 0.893, 0.941, 0.762, 0.976, 1.019, 0.889, 0.938, 0.961,
0.999, 0.88, 0.971, 0.924, 0.939, 0.966, 0.981, 0.974, 0.965,
0.938, 0.903, 1.094, 0.867, 1.007, 0.768, 0.731, 0.716, 0.693,
0.769, 0.731, 0.791, 0.765, 0.611, 0.745, 0.747, 0.755, 0.6,
0.796, 0.719, 0.458, 0.599, 0.635, 0.775, 0.66, 0.736, 0.745,
0.732, 0.653, 0.869, 0.834, 0.892, 0.542, 0.94, 0.814, 0.872,
0.772, 0.847, 0.946, 0.742, 0.959, 0.931, 0.967, 0.851, 1.061,
0.866, 0.838, 0.911, 0.885, 1.041, 0.94, 0.951, 0.836, 0.897,
0.738, 0.793, 0.764, 0.759, 0.872, 0.802, 0.857, 0.875, 0.83,
0.778, 0.86, 0.871, 0.794, 0.879, 0.674, 0.817, 0.913, 0.846,
0.931, 0.953, 0.809, 0.918, 0.85, 0.94, 0.871, 0.834, 0.963,
0.945, 0.966, 0.911, 0.934, 1.018, 0.948, 0.948, 0.947, 0.963,
0.837, 0.855, 0.815, 0.961, 0.92, 0.963, 0.812, 0.96, 0.982,
0.966, 0.874, 0.854, 0.9, 0.943, 0.898, 0.895, 0.938, 0.87, 0.878,
0.849, 0.802, 1.014, 1.043, 0.967, 0.967, 0.936, 0.996, 0.909,
0.962, 0.714, 0.979, 0.921, 1.01, 1.053, 1.006, 0.961, 1.043,
0.984, 1.034, 1.031, 0.894, 1.004, 0.97, 0.952, 0.932, 0.954,
1.03, 0.979, 0.89, 1.1, 0.965, 1.11, 0.965, 1.01, 1.08, 1.02,
0.812, 0.813, 0.833, 0.739, 0.819, 0.874, 0.8, 0.728, 0.774,
0.877, 0.564, 0.835, 0.722, 0.778, 0.81, 0.816, 0.838, 0.691,
0.695, 0.903, 0.731, 0.811, 0.77, 0.791, 0.928, 0.879, 0.906,
0.841, 0.906, 0.899, 0.888, 0.865, 0.914, 0.859, 0.927, 0.905,
0.936, 0.93, 0.845, 0.757, 0.868, 0.759, 0.931, 0.92, 0.921,
0.848, 1.016, 0.988, 0.9, 0.82, 0.949, 0.826, 0.828, 1.003, 0.924,
0.879, 0.913, 0.835, 0.876, 0.894, 0.841, 0.853, 0.733, 0.86,
0.814, 0.835, 0.995, 0.864, 0.87, 0.802, 0.875, 0.925, 0.813,
0.819, 0.961, 0.957, 0.903, 0.895, 0.876, 0.943, 0.882, 0.831,
0.911, 0.857, 0.912, 0.874, 0.867, 0.815, 0.832, 0.875, 0.876,
0.82, 0.857, 0.861, 0.858, 0.878, 0.836, 0.828, 0.903, 0.874,
0.957, 1.074, 0.993, 0.854, 1.043, 0.912, 0.952, 1.008, 1.048,
0.911, 0.998, 0.851, 0.868, 0.842, 0.744, 1.037, 1.027, 1.015,
1.033, 1.045, 1.026, 1.116, 0.794, 1.038, 1.105, 0.939, 0.809,
0.914, 0.772, 0.837, 0.96, 0.913, 0.883, 0.892, 0.831, 0.673,
0.75, 0.68, 0.501, 0.712, 0.662, 0.631, 0.647, 0.708, 0.657,
0.67, 0.716, 0.749, 0.672, 0.676, 0.682, 0.735, 0.717, 0.727,
0.679, 0.674, 0.673, 0.644, 0.605, 0.627, 0.457, 0.656, 0.665,
0.748, 0.755, 0.743, 0.744, 0.645, 0.574, 0.619, 0.607, 0.637,
0.573, 0.6, 0.623, 0.554, 0.658, 0.624, 0.582, 0.526, 0.467,
0.722, 0.419, 0.704, 0.718, 0.695, 0.867, 0.8, 0.827, 0.756,
0.677, 0.749, 0.686, 0.788, 0.719, 0.652, 0.771, 0.74, 0.57,
0.778, 0.809, 0.658, 0.758, 1.154, 0.76, 0.786, 0.828, 0.791,
0.644, 0.815, 0.73, 0.872, 0.943, 0.833, 0.809, 0.925, 0.866,
0.789, 0.857, 0.904, 0.793, 0.8, 0.822, 0.789, 0.711, 0.703,
0.654, 0.707, 0.674, 0.629, 0.772, 0.784, 0.861, 0.7, 0.763,
0.834, 0.645, 0.716, 0.712, 0.714, 0.942, 0.871, 0.973, 0.893,
0.788, 0.835, 0.917, 0.821, 0.87, 0.816, 0.848, 0.951, 0.874,
0.881, 0.804, 0.795, 0.733, 0.656, 0.85, 0.737, 0.851, 0.883,
0.814, 0.715, 0.728, 0.831, 0.792, 0.711, 0.895, 0.931, 0.916,
0.848, 0.654, 0.613, 0.842, 0.669, 0.831, 0.788, 0.835, 0.877,
0.954, 0.886, 0.895, 0.84, 0.723, 0.797, 0.836, 0.726, 0.727,
0.837, 0.715, 0.81, 0.812, 0.782, 0.757, 0.749, 0.779, 0.686,
0.76, 0.795, 0.785, 0.708, 0.66, 0.644, 0.785, 0.762, 0.744,
0.709, 0.718, 0.785, 0.82, 0.674, 0.811, 0.845, 0.693, 0.743,
0.723, 0.705, 0.607, 0.709, 0.715, 0.713, 0.765, 0.723, 0.82,
0.909, 0.925, 0.884, 0.788, 0.822, 0.816, 0.789, 0.877, 1, 0.817,
0.808, 0.879, 0.816, 0.939, 0.824, 0.922, 0.947, 0.792, 0.868,
0.526, 0.816, 0.77, 0.76, 0.771, 0.854, 0.921, 0.84, 0.844, 0.957,
0.929, 0.809, 0.883, 0.856, 0.755, 0.693, 0.914, 0.905, 0.86,
0.828, 0.678, 0.765, 0.518, 0.629, 0.763, 0.658, 0.732, 0.55,
0.633, 0.498, 0.633, 0.619, 0.521, 0.507, 0.696, 0.599, 0.605,
0.718, 0.615, 0.634, 0.514, 0.716, 0.708, 0.668, 0.693, 0.627,
0.623, 0.734, 0.599, 0.702, 0.678, 0.733, 0.656, 0.647, 0.719,
0.644, 0.628, 0.653, 0.597, 0.531, 0.624, 0.827, 0.846, 0.778,
0.764, 0.838, 0.841, 0.919, 0.852, 0.833, 0.665, 0.665, 0.563,
0.649, 0.617, 0.617, 0.55, 0.621, 0.607, 0.599, 1.025, 0.49,
1.004, 0.968, 0.687, 0.593, 0.631, 0.713, 0.747, 0.685, 0.791,
0.66, 0.64, 0.756, 0.727, 0.619, 0.601, 0.665, 0.572, 0.472,
0.561, 0.637, 0.627, 0.621, 0.637, 0.596, 0.591, 0.686, 0.685,
0.464, 0.649, 0.595, 0.523, 0.64, 0.618, 0.566, 0.539, 0.606,
0.585, 0.684, 0.748, 0.701, 0.484, 0.779, 0.734, 0.777, 0.893,
0.812, 0.852, 0.77, 0.734, 0.733, 0.751, 0.686, 0.762, 0.798,
0.804, 0.779, 0.731, 0.83, 0.644, 0.764, 0.75, 0.721, 0.669,
0.716, 0.717, 0.629, 0.69, 0.755, 0.663, 0.618, 0.717, 0.69,
0.675, 0.677, 0.727, 0.677, 0.623, 0.618, 0.641, 0.592, 0.768,
0.816, 0.498, 0.644, 0.743, 0.662, 0.713, 0.411, 0.768, 0.665,
0.711, 0.719, 0.71, 0.722, 0.603, 0.662, 0.649, 0.614, 0.402,
0.507, 0.451, 0.517, 0.475, 0.576, 0.651, 0.618, 0.539, 0.705,
0.687, 0.847, 0.817, 0.842, 0.699, 0.838, 0.801, 0.635, 0.761,
0.696, 0.573, 0.656, 0.688, 0.689, 0.587, 0.608, 0.643, 0.618,
1.198, 0.758, 0.929, 0.93, 1.005, 0.917, 0.91, 0.733, 0.731,
0.81, 0.934, 0.785, 0.799, 0.742, 0.921, 0.957, 0.894, 0.87,
0.791, 0.883, 0.9, 0.756, 0.776, 0.857, 0.769, 0.89, 0.811, 1.002,
0.877, 0.79, 0.902, 0.864, 0.856, 0.797, 0.839, 0.867, 0.724,
0.829), Malformation = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
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0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("Litter",
"Dose", "Weight", "Malformation"), class = "data.frame", row.names = c(NA,
-1028L))
## ----pupEG0, echo=FALSE,results='asis'----------------------------------------
# data("wuleon", package="SiTuR")
wuleonTab<-xtable(wuleon, caption="Raw data of a fetal weight and malformation", label="tab:wu")
print(wuleonTab, include.rownames=FALSE, table.placement = "H",
add.to.row=list(pos=list(18:1018, 1019),
command=c("%", "$\\hdots$ & $\\hdots$ & $\\hdots$ & $\\hdots$ \\\\ \n")))
## ----pupEG, echo=FALSE, fig.cap='Ethylen glycol example', fig.pos="H", fig.width=6.5, fig.height=4.5, dev="png", dpi=300----
wuleon$Mal<- factor(wuleon$Malformation)
wuleon$Litter<- factor(wuleon$Litter)
boxplot(Weight~factor(Dose), data=wuleon)
points(y=wuleon$Weight, x=factor(wuleon$Dose), col=c("blue", "black")[wuleon$Mal])
## ----pupEGM, echo=TRUE, results='asis'----------------------------------------
wuleon$dose<-as.factor(wuleon$Dose)
fitwu <- lm(Weight~Dose, data=wuleon)
ttwu <- tukeytrendfit(fitwu, dose="Dose", scaling=c("ari", "ord", "arilog"))
fitwm <- glm(Malformation~Dose, family=binomial(),data=wuleon)
ttwm <- tukeytrendfit(fitwm, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttwum <- combtt(ttwu, ttwm) # combine both models
Exawu<-summary(glht(model=cttwum$mmm, linfct=cttwum$mlf))
## ----pupEGM1, echo=FALSE, results='asis'--------------------------------------
Twu <- data.frame(names(Exawu$test$tstat),Exawu$test$tstat, Exawu$test$pvalues)
colnames(Twu)<-c("Model","Test stats", "p-value")
print(xtable(Twu, digits=c(2,2,2,7), caption="Tukey trend test for joint modeling of normal and binomial endpoint", label="tab:wuL"), include.rownames=FALSE)
## ----Exa16, echo=TRUE, results='asis', warning=FALSE, message=FALSE-----------
adverse <-c(rep("absent",11), rep("present",9), rep("absent",24), rep("present",19),
rep("absent",21),rep("present",21),rep("absent",21),rep("present",21),
rep("absent",17),rep("present",24))
#dose <-c(rep("0", 20), rep("0.125", 43),rep("0.5", 42),rep("0.75", 42), rep(1, 41))
dose <- c(rep(0, 20), rep(0.125, 43),rep(0.5, 42),rep(0.75, 42), rep(1, 41))
webster <- data.frame(adverse=factor(adverse),dose=dose)
t(table(webster))
lmW <-glm(adverse~dose, data=webster, family= binomial(link="logit"))
EX16 <- tukeytrendfit(lmW, dose="dose", scaling=c("ari", "ord", "arilog"))
EXA16 <- summary(glht(model=EX16$mmm, linfct=EX16$mlf))
## ----multRepDATA, echo=FALSE, fig.cap='Multiple overdispersed proportions.', message=FALSE, fig.pos="H", warning=FALSE----
if(requireNamespace("CorrBin")){
data(dehp, package="CorrBin")
Dehp <- dehp[rep(1:nrow(dehp),times=dehp$Freq),]
Dehp$p1<-Dehp$NResp.1/Dehp$ClusterSize
Dehp$p2<-Dehp$NResp.2/Dehp$ClusterSize
Dehp$p3<-Dehp$NResp.3/Dehp$ClusterSize
Dehp$Dose<-as.numeric(paste(Dehp$Trt))
MmN <- glm(cbind(NResp.1, ClusterSize-NResp.1)~Dose, data=Dehp, family= quasibinomial(link="logit"))
DmN <- glm(cbind(NResp.2, ClusterSize-NResp.2)~Dose, data=Dehp, family= quasibinomial(link="logit"))
NmN <- glm(cbind(NResp.3, ClusterSize-NResp.3)~Dose, data=Dehp, family= quasibinomial(link="logit"))
tMmN <- tukeytrendfit(MmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
tDmN <- tukeytrendfit(DmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
tNmN <- tukeytrendfit(NmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
ctMDN <- combtt(tMmN, tDmN, tNmN)
Exa18<-summary(glht(model=ctMDN$mmm, linfct=ctMDN$mlf))
Exa18
}
## ----Exa20data, echo=FALSE,results='hide', warning=FALSE, message=FALSE-------
mn <- structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L), .Label = c("Control", "D188", "D375", "D750", "Positive"
), class = "factor"), animal = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 22, 23, 24),
MN = c(4, 2, 4, 2, 2, 3, 5, 7, 2, 0, 5, 6, 1, 4, 2, 2, 4,
1, 1, 0, 26, 28, 22, 37, 29)), .Names = c("group", "animal",
"MN"), row.names = c(NA, 25L), class = "data.frame")
## ----Exa20, echo=TRUE,results='asis', warning=FALSE, message=FALSE------------
#data("mn", package="SiTuR")
Mn<-droplevels(mn[mn$group != "Positive", ])
Mn$dose<-c(rep(0,5), rep(188, 5), rep(375,5), rep(750,5))
fitMN <- glm(MN ~ dose, data=Mn, family=quasipoisson(link="log"))
Exa20 <- tukeytrendfit(fitMN, dose="dose", scaling=c("ari", "ord", "arilog", "treat"),
ctype="UmbrellaWilliams")
EXA20<-summary(glht(model=Exa20$mmm, linfct=Exa20$mlf))
## ----OrderedDATA, echo=FALSE, results='hide', warning=FALSE, message=FALSE----
hyalin <-
structure(list(dose = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("0", "5", "10",
"15", "20", "30", "45", "90"), class = "factor"), droplets = c(1,
1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 4, 3,
3, 3, 4, 4, 4)), .Names = c("dose", "droplets"), class = "data.frame", row.names = c(NA,
48L))
## ----Ordered, echo=TRUE, results='asis', warning=FALSE, message=FALSE---------
#data("hyalin", package="SiTuR")
exP<-glm(droplets~dose, data=hyalin, family=quasipoisson(link = "log"))
ntab <- table(hyalin$dose) # sample sizes needed for unbalanced designs
cmd <- contrMat(n=ntab, type="Dunnett")
cmuw <- contrMat(n=ntab, type="UmbrellaWilliams")
cmduw <- rbind(cmd, cmuw) # combine both matrices
aa<-summary(glht(exP, linfct=mcp(dose=cmduw),alternative="greater"))
DlevelO<-as.numeric(levels(hyalin$dose))
S0O<-log(DlevelO[2])-log(DlevelO[3]/DlevelO[2])*(DlevelO[2]-DlevelO[1])/(DlevelO[3]-DlevelO[2])
hyalin$DoseN<-as.numeric(as.character(hyalin$dose))
hyalin$DoseO<-as.numeric(hyalin$dose)
hyalin$DoseL<-log(hyalin$DoseN)
hyalin$DoseLL<-hyalin$DoseL
hyalin$DoseLL[hyalin$DoseN==DlevelO[1]] <-S0O
# umbrella needed?
lmN <-glm(droplets~DoseN, data=hyalin,family=quasipoisson(link = "log"))
lmO <-glm(droplets~DoseO, data=hyalin, family=quasipoisson(link = "log"))
lmLL <-glm(droplets~DoseLL, data=hyalin, family=quasipoisson(link = "log"))
ordC <- summary(glht(mmm(covariate=lmN, ordinal=lmO, linlog=lmLL),
mlf(covariate="DoseN=0", ordinal="DoseO=0", linlog="DoseLL=0")))
## ----Ordered1, echo=FALSE,results='asis', warning=FALSE, message=FALSE--------
Tord <- data.frame(names(ordC$test$tstat),ordC$test$tstat, ordC$test$pvalues)
colnames(Tord)<-c("Model","Test stats", "p-value")
print(xtable(Tord, digits=7, caption="Tukey trend test for ordered categorical data",
label="tab:order"), include.rownames=FALSE)
## ----weitere7, echo=TRUE, results='asis', warning=FALSE, message=FALSE--------
if(requireNamespace("HSAUR3")){
data(bp, package="HSAUR3")
bp$Dose<-10^(bp$logdose)
fitbp <-lm(bloodp~Dose+recovtime, data=bp)
ttbp <- tukeytrendfit(fitbp, dose="Dose", scaling=c("ari", "ord", "log"))
EXA31 <- summary(glht(model=ttbp$mmm, linfct=ttbp$mlf))
EXA31}
## ----Exam21a, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
if(requireNamespace("SASmixed")){
data(AvgDailyGain, package="SASmixed")
AVG<-AvgDailyGain
library("lme4")
mmNN <-lmer(adg~Trt+InitWt+(1|Block), data=AVG)
tt21 <- tukeytrendfit(mmNN, dose="Trt", scaling=c("ari", "ord", "arilog"),
ddf="residual", d0shift=c(0.1, 0.5, 1, 5) )
comptt21 <- glht(model=tt21$mmm, linfct=tt21$mlf)
Exa21a<-summary(comptt21)
Exa21a
}
## ----atladat, echo=FALSE,results='asis', warning=FALSE, message=FALSE---------
atla <-
structure(list(dose = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L), centre = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L), weight = c(10.2, 8.8, 10.4, 9, 8.9, 8.4, 10.7, 9.3, 9.1,
8.7, 11.6, 9.9, 9.92, 9.5, 9.9, 9.9, 9.7, 10, 10.6, 8.2, 12.6,
11.4, 11.9, 12.1, 10.5, 12.8, 11.8, 10.3, 12.4, 12.6, 10.6, 13.1,
15.6, 12.6, 13.1, 13, 12.9, 14.9, 14.4, 12.5, 6.23, 5.99, 6.3,
5.49, 5.15, 6.03, 6.72, 5.8, 6.12, 5.82, 7.19, 5.83, 6.75, 5.98,
6.38, 6.36, 6.55, 6.82, 5.56, 7.13, 9.45, 7.53, 8.81, 8.06, 9.83,
8.98, 7.653, 8.99, 8.82, 8.79, 10.27, 10.67, 10.73, 9.46, 8.94,
10.9, 10.95, 10.93, 10.36, 9.96, 7.93, 9.14, 7.58, 9.14, 7.33,
8.05, 7.25, 7.11, 6.86, 7.09, 7.31, 8.09, 7.65, 7.27, 7.14, 7.37,
8.8, 7.87, 8.25, 7.22, 8.89, 8.46, 8.43, 9.37, 9.68, 10.27, 9.06,
9.74, 8.37, 9.18, 12.48, 12.16, 11.39, 10.66, 11.98, 13.66, 11.23,
12.98, 10.72, 11.23, 7.16, 5.9, 5.62, 7.02, 6.84, 6.12, 6.42,
6.03, 6.29, 7.36, 6.99, 6.45, 7.73, 6.33, 7.15, 6.91, 6.09, 7.35,
6.92, 10.02, 9.09, 9.15, 7.82, 8.94, 8.45, 6.84, 10.21, 11.62,
8.51, 11.14, 11.77, 10.11, 10.2, 11.25, 9.92, 10.54, 10.68, 11.464,
10.39, 6.7, 5.9, 5.7, 6.3, 6.9, 5.4, 5.7, 6.5, 5.9, 5.8, 6.5,
6.5, 6.6, 5.7, 5.6, 5.8, 7.2, 6.8, 6.8, 6.2, 9, 8.7, 7.7, 8.3,
8.6, 8, 7.8, 9.3, 8, 8.4, 10.7, 11.6, 8.5, 9.7, 11, 11.8, 11.9,
11, 10.5, 11.3, 7.09, 7.99, 8.02, 6.75, 7.2, 6.88, 7.16, 8.05,
7.77, 7.51, 8.11, 7.64, 6.59, 6.87, 8.02, 6.08, 7.3, 8.79, 8.1,
7.86, 9.99, 9.4, 8.88, 8.22, 9.54, 9.01, 9.69, 8.55, 8.77, 7.77,
12.55, 12.43, 9.67, 10.66, 10.94, 12.83, 11.33, 7.1, 10.12, 9.34,
9.52, 8.75, 8.88, 9.15, 10.43, 9.63, 7.81, 8.69, 9.08, 11.23,
8.19, 9.11, 8.07, 8.65, 8.08, 9.51, 8.59, 11.16, 12.82, 11.69,
10.4, 11.29, 12.36, 11.91, 12.46, 11.21, 10.22, 16.56, 11.87,
11.56, 13.17, 11.56, 14.06, 12.22, 11.36, 12.2, 13.2, 6.65, 5.68,
6.8, 6.61, 5.34, 6.11, 5.87, 6.66, 6.25, 6.4, 6.67, 6.06, 6.71,
6.83, 5.98, 5.61, 6.12, 6.96, 6.68, 7.43, 8.67, 8.01, 7.71, 7.94,
7.38, 8.28, 8.68, 8.15, 6.82, 6.65, 8.33, 7.6, 9.08, 9.74, 7.66,
8.51, 8.41, 9.36, 9.86, 8.75)), .Names = c("dose", "centre",
"weight"), class = "data.frame", row.names = c(NA, -316L))
atla$Dose[atla$dose==4] <-100
atla$Dose[atla$dose==3] <-30
atla$Dose[atla$dose==2] <-3
atla$Dose[atla$dose==1] <-0
atla$DOSE<-as.factor(atla$Dose)
## ----weitere2a, echo=TRUE,results='asis', warning=FALSE, message=FALSE--------
library("lme4")
mixedA <- lmer(weight ~ Dose+ (1|centre), data=atla)
tt24 <- tukeytrendfit(mixedA, dose="Dose", scaling=c("ari", "ord", "arilog"),
ddf="KR")
comptt24 <- glht(model=tt24$mmm, linfct=tt24$mlf)
Exa24<-summary(comptt24)
## ----weitere2p, echo=FALSE,results='asis', warning=FALSE, message=FALSE-------
exa24 <- data.frame(names(Exa24$test$tstat),Exa24$test$tstat, Exa24$test$pvalues)
colnames(exa24)<-c("Comparison","Test stats", "p-value")
print(xtable(exa24, digits=c(2,2,2,8), caption="Multicentre trial: mixed model",
label="tab:exa24"), include.rownames=FALSE)
## ----ComBox1DATA, echo=FALSE, results='hide',message=FALSE, warning=FALSE-----
TIComet <-
structure(list(Sample = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), Treatment = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L), .Label = c("1", "2", "3", "4", "5", "6", "7",
"8", "9", "10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
"28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47",
"48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67",
"68", "69", "70", "71", "72", "73", "74", "75", "76", "77",
"78", "79", "80", "81", "82", "83", "84", "85", "86", "87",
"88", "89", "90", "91", "92", "93", "94", "95", "96", "97",
"98", "99", "100"), class = "factor"), Animal_no = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
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956L, 957L, 958L, 959L, 960L, 961L, 962L, 963L, 964L, 965L, 966L,
967L, 968L, 969L, 970L, 971L, 972L, 973L, 974L, 975L, 976L, 977L,
978L, 979L, 980L, 981L, 982L, 983L, 984L, 985L, 986L, 987L, 988L,
989L, 990L, 991L, 992L, 993L, 994L, 995L, 996L, 997L, 998L, 999L,
1000L, 1051L, 1052L, 1053L, 1054L, 1055L, 1056L, 1057L, 1058L,
1059L, 1060L, 1061L, 1062L, 1063L, 1064L, 1065L, 1066L, 1067L,
1068L, 1069L, 1070L, 1071L, 1072L, 1073L, 1074L, 1075L, 1076L,
1077L, 1078L, 1079L, 1080L, 1081L, 1082L, 1083L, 1084L, 1085L,
1086L, 1087L, 1088L, 1089L, 1090L, 1091L, 1092L, 1093L, 1094L,
1095L, 1096L, 1097L, 1098L, 1099L, 1100L, 1101L, 1102L, 1103L,
1104L, 1105L, 1106L, 1107L, 1108L, 1109L, 1110L, 1111L, 1112L,
1113L, 1114L, 1115L, 1116L, 1117L, 1118L, 1119L, 1120L, 1121L,
1122L, 1123L, 1124L, 1125L, 1126L, 1127L, 1128L, 1129L, 1130L,
1131L, 1132L, 1133L, 1134L, 1135L, 1136L, 1137L, 1138L, 1139L,
1140L, 1141L, 1142L, 1143L, 1144L, 1145L, 1146L, 1147L, 1148L,
1149L, 1150L, 1201L, 1202L, 1203L, 1204L, 1205L, 1206L, 1207L,
1208L, 1209L, 1210L, 1211L, 1212L, 1213L, 1214L, 1215L, 1216L,
1217L, 1218L, 1219L, 1220L, 1221L, 1222L, 1223L, 1224L, 1225L,
1226L, 1227L, 1228L, 1229L, 1230L, 1231L, 1232L, 1233L, 1234L,
1235L, 1236L, 1237L, 1238L, 1239L, 1240L, 1241L, 1242L, 1243L,
1244L, 1245L, 1246L, 1247L, 1248L, 1249L, 1250L, 1251L, 1252L,
1253L, 1254L, 1255L, 1256L, 1257L, 1258L, 1259L, 1260L, 1261L,
1262L, 1263L, 1264L, 1265L, 1266L, 1267L, 1268L, 1269L, 1270L,
1271L, 1272L, 1273L, 1274L, 1275L, 1276L, 1277L, 1278L, 1279L,
1280L, 1281L, 1282L, 1283L, 1284L, 1285L, 1286L, 1287L, 1288L,
1289L, 1290L, 1291L, 1292L, 1293L, 1294L, 1295L, 1296L, 1297L,
1298L, 1299L, 1300L, 1351L, 1352L, 1353L, 1354L, 1355L, 1356L,
1357L, 1358L, 1359L, 1360L, 1361L, 1362L, 1363L, 1364L, 1365L,
1366L, 1367L, 1368L, 1369L, 1370L, 1371L, 1372L, 1373L, 1374L,
1375L, 1376L, 1377L, 1378L, 1379L, 1380L, 1381L, 1382L, 1383L,
1384L, 1385L, 1386L, 1387L, 1388L, 1389L, 1390L, 1391L, 1392L,
1393L, 1394L, 1395L, 1396L, 1397L, 1398L, 1399L, 1400L, 1401L,
1402L, 1403L, 1404L, 1405L, 1406L, 1407L, 1408L, 1409L, 1410L,
1411L, 1412L, 1413L, 1414L, 1415L, 1416L, 1417L, 1418L, 1419L,
1420L, 1421L, 1422L, 1423L, 1424L, 1425L, 1426L, 1427L, 1428L,
1429L, 1430L, 1431L, 1432L, 1433L, 1434L, 1435L, 1436L, 1437L,
1438L, 1439L, 1440L, 1441L, 1442L, 1443L, 1444L, 1445L, 1446L,
1447L, 1448L, 1449L, 1450L, 1501L, 1502L, 1503L, 1504L, 1505L,
1506L, 1507L, 1508L, 1509L, 1510L, 1511L, 1512L, 1513L, 1514L,
1515L, 1516L, 1517L, 1518L, 1519L, 1520L, 1521L, 1522L, 1523L,
1524L, 1525L, 1526L, 1527L, 1528L, 1529L, 1530L, 1531L, 1532L,
1533L, 1534L, 1535L, 1536L, 1537L, 1538L, 1539L, 1540L, 1541L,
1542L, 1543L, 1544L, 1545L, 1546L, 1547L, 1548L, 1549L, 1550L,
1551L, 1552L, 1553L, 1554L, 1555L, 1556L, 1557L, 1558L, 1559L,
1560L, 1561L, 1562L, 1563L, 1564L, 1565L, 1566L, 1567L, 1568L,
1569L, 1570L, 1571L, 1572L, 1573L, 1574L, 1575L, 1576L, 1577L,
1578L, 1579L, 1580L, 1581L, 1582L, 1583L, 1584L, 1585L, 1586L,
1587L, 1588L, 1589L, 1590L, 1591L, 1592L, 1593L, 1594L, 1595L,
1596L, 1597L, 1598L, 1599L, 1600L, 1651L, 1652L, 1653L, 1654L,
1655L, 1656L, 1657L, 1658L, 1659L, 1660L, 1661L, 1662L, 1663L,
1664L, 1665L, 1666L, 1667L, 1668L, 1669L, 1670L, 1671L, 1672L,
1673L, 1674L, 1675L, 1676L, 1677L, 1678L, 1679L, 1680L, 1681L,
1682L, 1683L, 1684L, 1685L, 1686L, 1687L, 1688L, 1689L, 1690L,
1691L, 1692L, 1693L, 1694L, 1695L, 1696L, 1697L, 1698L, 1699L,
1700L, 1701L, 1702L, 1703L, 1704L, 1705L, 1706L, 1707L, 1708L,
1709L, 1710L, 1711L, 1712L, 1713L, 1714L, 1715L, 1716L, 1717L,
1718L, 1719L, 1720L, 1721L, 1722L, 1723L, 1724L, 1725L, 1726L,
1727L, 1728L, 1729L, 1730L, 1731L, 1732L, 1733L, 1734L, 1735L,
1736L, 1737L, 1738L, 1739L, 1740L, 1741L, 1742L, 1743L, 1744L,
1745L, 1746L, 1747L, 1748L, 1749L, 1750L, 1801L, 1802L, 1803L,
1804L, 1805L, 1806L, 1807L, 1808L, 1809L, 1810L, 1811L, 1812L,
1813L, 1814L, 1815L, 1816L, 1817L, 1818L, 1819L, 1820L, 1821L,
1822L, 1823L, 1824L, 1825L, 1826L, 1827L, 1828L, 1829L, 1830L,
1831L, 1832L, 1833L, 1834L, 1835L, 1836L, 1837L, 1838L, 1839L,
1840L, 1841L, 1842L, 1843L, 1844L, 1845L, 1846L, 1847L, 1848L,
1849L, 1850L, 1851L, 1852L, 1853L, 1854L, 1855L, 1856L, 1857L,
1858L, 1859L, 1860L, 1861L, 1862L, 1863L, 1864L, 1865L, 1866L,
1867L, 1868L, 1869L, 1870L, 1871L, 1872L, 1873L, 1874L, 1875L,
1876L, 1877L, 1878L, 1879L, 1880L, 1881L, 1882L, 1883L, 1884L,
1885L, 1886L, 1887L, 1888L, 1889L, 1890L, 1891L, 1892L, 1893L,
1894L, 1895L, 1896L, 1897L, 1898L, 1899L, 1900L, 1951L, 1952L,
1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L, 1960L, 1961L,
1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L,
1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L,
1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L,
1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L,
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L,
2016L, 2017L, 2018L, 2019L, 2020L, 2021L, 2022L, 2023L, 2024L,
2025L, 2026L, 2027L, 2028L, 2029L, 2030L, 2031L, 2032L, 2033L,
2034L, 2035L, 2036L, 2037L, 2038L, 2039L, 2040L, 2041L, 2042L,
2043L, 2044L, 2045L, 2046L, 2047L, 2048L, 2049L, 2050L, 2101L,
2102L, 2103L, 2104L, 2105L, 2106L, 2107L, 2108L, 2109L, 2110L,
2111L, 2112L, 2113L, 2114L, 2115L, 2116L, 2117L, 2118L, 2119L,
2120L, 2121L, 2122L, 2123L, 2124L, 2125L, 2126L, 2127L, 2128L,
2129L, 2130L, 2131L, 2132L, 2133L, 2134L, 2135L, 2136L, 2137L,
2138L, 2139L, 2140L, 2141L, 2142L, 2143L, 2144L, 2145L, 2146L,
2147L, 2148L, 2149L, 2150L, 2151L, 2152L, 2153L, 2154L, 2155L,
2156L, 2157L, 2158L, 2159L, 2160L, 2161L, 2162L, 2163L, 2164L,
2165L, 2166L, 2167L, 2168L, 2169L, 2170L, 2171L, 2172L, 2173L,
2174L, 2175L, 2176L, 2177L, 2178L, 2179L, 2180L, 2181L, 2182L,
2183L, 2184L, 2185L, 2186L, 2187L, 2188L, 2189L, 2190L, 2191L,
2192L, 2193L, 2194L, 2195L, 2196L, 2197L, 2198L, 2199L, 2200L,
2251L, 2252L, 2253L, 2254L, 2255L, 2256L, 2257L, 2258L, 2259L,
2260L, 2261L, 2262L, 2263L, 2264L, 2265L, 2266L, 2267L, 2268L,
2269L, 2270L, 2271L, 2272L, 2273L, 2274L, 2275L, 2276L, 2277L,
2278L, 2279L, 2280L, 2281L, 2282L, 2283L, 2284L, 2285L, 2286L,
2287L, 2288L, 2289L, 2290L, 2291L, 2292L, 2293L, 2294L, 2295L,
2296L, 2297L, 2298L, 2299L, 2300L, 2301L, 2302L, 2303L, 2304L,
2305L, 2306L, 2307L, 2308L, 2309L, 2310L, 2311L, 2312L, 2313L,
2314L, 2315L, 2316L, 2317L, 2318L, 2319L, 2320L, 2321L, 2322L,
2323L, 2324L, 2325L, 2326L, 2327L, 2328L, 2329L, 2330L, 2331L,
2332L, 2333L, 2334L, 2335L, 2336L, 2337L, 2338L, 2339L, 2340L,
2341L, 2342L, 2343L, 2344L, 2345L, 2346L, 2347L, 2348L, 2349L,
2350L, 2401L, 2402L, 2403L, 2404L, 2405L, 2406L, 2407L, 2408L,
2409L, 2410L, 2411L, 2412L, 2413L, 2414L, 2415L, 2416L, 2417L,
2418L, 2419L, 2420L, 2421L, 2422L, 2423L, 2424L, 2425L, 2426L,
2427L, 2428L, 2429L, 2430L, 2431L, 2432L, 2433L, 2434L, 2435L,
2436L, 2437L, 2438L, 2439L, 2440L, 2441L, 2442L, 2443L, 2444L,
2445L, 2446L, 2447L, 2448L, 2449L, 2450L, 2451L, 2452L, 2453L,
2454L, 2455L, 2456L, 2457L, 2458L, 2459L, 2460L, 2461L, 2462L,
2463L, 2464L, 2465L, 2466L, 2467L, 2468L, 2469L, 2470L, 2471L,
2472L, 2473L, 2474L, 2475L, 2476L, 2477L, 2478L, 2479L, 2480L,
2481L, 2482L, 2483L, 2484L, 2485L, 2486L, 2487L, 2488L, 2489L,
2490L, 2491L, 2492L, 2493L, 2494L, 2495L, 2496L, 2497L, 2498L,
2499L, 2500L, 2551L, 2552L, 2553L, 2554L, 2555L, 2556L, 2557L,
2558L, 2559L, 2560L, 2561L, 2562L, 2563L, 2564L, 2565L, 2566L,
2567L, 2568L, 2569L, 2570L, 2571L, 2572L, 2573L, 2574L, 2575L,
2576L, 2577L, 2578L, 2579L, 2580L, 2581L, 2582L, 2583L, 2584L,
2585L, 2586L, 2587L, 2588L, 2589L, 2590L, 2591L, 2592L, 2593L,
2594L, 2595L, 2596L, 2597L, 2598L, 2599L, 2600L, 2601L, 2602L,
2603L, 2604L, 2605L, 2606L, 2607L, 2608L, 2609L, 2610L, 2611L,
2612L, 2613L, 2614L, 2615L, 2616L, 2617L, 2618L, 2619L, 2620L,
2621L, 2622L, 2623L, 2624L, 2625L, 2626L, 2627L, 2628L, 2629L,
2630L, 2631L, 2632L, 2633L, 2634L, 2635L, 2636L, 2637L, 2638L,
2639L, 2640L, 2641L, 2642L, 2643L, 2644L, 2645L, 2646L, 2647L,
2648L, 2649L, 2650L, 2701L, 2702L, 2703L, 2704L, 2705L, 2706L,
2707L, 2708L, 2709L, 2710L, 2711L, 2712L, 2713L, 2714L, 2715L,
2716L, 2717L, 2718L, 2719L, 2720L, 2721L, 2722L, 2723L, 2724L,
2725L, 2726L, 2727L, 2728L, 2729L, 2730L, 2731L, 2732L, 2733L,
2734L, 2735L, 2736L, 2737L, 2738L, 2739L, 2740L, 2741L, 2742L,
2743L, 2744L, 2745L, 2746L, 2747L, 2748L, 2749L, 2750L, 2751L,
2752L, 2753L, 2754L, 2755L, 2756L, 2757L, 2758L, 2759L, 2760L,
2761L, 2762L, 2763L, 2764L, 2765L, 2766L, 2767L, 2768L, 2769L,
2770L, 2771L, 2772L, 2773L, 2774L, 2775L, 2776L, 2777L, 2778L,
2779L, 2780L, 2781L, 2782L, 2783L, 2784L, 2785L, 2786L, 2787L,
2788L, 2789L, 2790L, 2791L, 2792L, 2793L, 2794L, 2795L, 2796L,
2797L, 2798L, 2799L, 2800L, 2851L, 2852L, 2853L, 2854L, 2855L,
2856L, 2857L, 2858L, 2859L, 2860L, 2861L, 2862L, 2863L, 2864L,
2865L, 2866L, 2867L, 2868L, 2869L, 2870L, 2871L, 2872L, 2873L,
2874L, 2875L, 2876L, 2877L, 2878L, 2879L, 2880L, 2881L, 2882L,
2883L, 2884L, 2885L, 2886L, 2887L, 2888L, 2889L, 2890L, 2891L,
2892L, 2893L, 2894L, 2895L, 2896L, 2897L, 2898L, 2899L, 2900L,
2901L, 2902L, 2903L, 2904L, 2905L, 2906L, 2907L, 2908L, 2909L,
2910L, 2911L, 2912L, 2913L, 2914L, 2915L, 2916L, 2917L, 2918L,
2919L, 2920L, 2921L, 2922L, 2923L, 2924L, 2925L, 2926L, 2927L,
2928L, 2929L, 2930L, 2931L, 2932L, 2933L, 2934L, 2935L, 2936L,
2937L, 2938L, 2939L, 2940L, 2941L, 2942L, 2943L, 2944L, 2945L,
2946L, 2947L, 2948L, 2949L, 2950L, 3001L, 3002L, 3003L, 3004L,
3005L, 3006L, 3007L, 3008L, 3009L, 3010L, 3011L, 3012L, 3013L,
3014L, 3015L, 3016L, 3017L, 3018L, 3019L, 3020L, 3021L, 3022L,
3023L, 3024L, 3025L, 3026L, 3027L, 3028L, 3029L, 3030L, 3031L,
3032L, 3033L, 3034L, 3035L, 3036L, 3037L, 3038L, 3039L, 3040L,
3041L, 3042L, 3043L, 3044L, 3045L, 3046L, 3047L, 3048L, 3049L,
3050L, 3051L, 3052L, 3053L, 3054L, 3055L, 3056L, 3057L, 3058L,
3059L, 3060L, 3061L, 3062L, 3063L, 3064L, 3065L, 3066L, 3067L,
3068L, 3069L, 3070L, 3071L, 3072L, 3073L, 3074L, 3075L, 3076L,
3077L, 3078L, 3079L, 3080L, 3081L, 3082L, 3083L, 3084L, 3085L,
3086L, 3087L, 3088L, 3089L, 3090L, 3091L, 3092L, 3093L, 3094L,
3095L, 3096L, 3097L, 3098L, 3099L, 3100L, 3151L, 3152L, 3153L,
3154L, 3155L, 3156L, 3157L, 3158L, 3159L, 3160L, 3161L, 3162L,
3163L, 3164L, 3165L, 3166L, 3167L, 3168L, 3169L, 3170L, 3171L,
3172L, 3173L, 3174L, 3175L, 3176L, 3177L, 3178L, 3179L, 3180L,
3181L, 3182L, 3183L, 3184L, 3185L, 3186L, 3187L, 3188L, 3189L,
3190L, 3191L, 3192L, 3193L, 3194L, 3195L, 3196L, 3197L, 3198L,
3199L, 3200L, 3201L, 3202L, 3203L, 3204L, 3205L, 3206L, 3207L,
3208L, 3209L, 3210L, 3211L, 3212L, 3213L, 3214L, 3215L, 3216L,
3217L, 3218L, 3219L, 3220L, 3221L, 3222L, 3223L, 3224L, 3225L,
3226L, 3227L, 3228L, 3229L, 3230L, 3231L, 3232L, 3233L, 3234L,
3235L, 3236L, 3237L, 3238L, 3239L, 3240L, 3241L, 3242L, 3243L,
3244L, 3245L, 3246L, 3247L, 3248L, 3249L, 3250L, 3301L, 3302L,
3303L, 3304L, 3305L, 3306L, 3307L, 3308L, 3309L, 3310L, 3311L,
3312L, 3313L, 3314L, 3315L, 3316L, 3317L, 3318L, 3319L, 3320L,
3321L, 3322L, 3323L, 3324L, 3325L, 3326L, 3327L, 3328L, 3329L,
3330L, 3331L, 3332L, 3333L, 3334L, 3335L, 3336L, 3337L, 3338L,
3339L, 3340L, 3341L, 3342L, 3343L, 3344L, 3345L, 3346L, 3347L,
3348L, 3349L, 3350L, 3351L, 3352L, 3353L, 3354L, 3355L, 3356L,
3357L, 3358L, 3359L, 3360L, 3361L, 3362L, 3363L, 3364L, 3365L,
3366L, 3367L, 3368L, 3369L, 3370L, 3371L, 3372L, 3373L, 3374L,
3375L, 3376L, 3377L, 3378L, 3379L, 3380L, 3381L, 3382L, 3383L,
3384L, 3385L, 3386L, 3387L, 3388L, 3389L, 3390L, 3391L, 3392L,
3393L, 3394L, 3395L, 3396L, 3397L, 3398L, 3399L, 3400L, 3451L,
3452L, 3453L, 3454L, 3455L, 3456L, 3457L, 3458L, 3459L, 3460L,
3461L, 3462L, 3463L, 3464L, 3465L, 3466L, 3467L, 3468L, 3469L,
3470L, 3471L, 3472L, 3473L, 3474L, 3475L, 3476L, 3477L, 3478L,
3479L, 3480L, 3481L, 3482L, 3483L, 3484L, 3485L, 3486L, 3487L,
3488L, 3489L, 3490L, 3491L, 3492L, 3493L, 3494L, 3495L, 3496L,
3497L, 3498L, 3499L, 3500L, 3501L, 3502L, 3503L, 3504L, 3505L,
3506L, 3507L, 3508L, 3509L, 3510L, 3511L, 3512L, 3513L, 3514L,
3515L, 3516L, 3517L, 3518L, 3519L, 3520L, 3521L, 3522L, 3523L,
3524L, 3525L, 3526L, 3527L, 3528L, 3529L, 3530L, 3531L, 3532L,
3533L, 3534L, 3535L, 3536L, 3537L, 3538L, 3539L, 3540L, 3541L,
3542L, 3543L, 3544L, 3545L, 3546L, 3547L, 3548L, 3549L, 3550L,
3601L, 3602L, 3603L, 3604L, 3605L, 3606L, 3607L, 3608L, 3609L,
3610L, 3611L, 3612L, 3613L, 3614L, 3615L, 3616L, 3617L, 3618L,
3619L, 3620L, 3621L, 3622L, 3623L, 3624L, 3625L, 3626L, 3627L,
3628L, 3629L, 3630L, 3631L, 3632L, 3633L, 3634L, 3635L, 3636L,
3637L, 3638L, 3639L, 3640L, 3641L, 3642L, 3643L, 3644L, 3645L,
3646L, 3647L, 3648L, 3649L, 3650L, 3651L, 3652L, 3653L, 3654L,
3655L, 3656L, 3657L, 3658L, 3659L, 3660L, 3661L, 3662L, 3663L,
3664L, 3665L, 3666L, 3667L, 3668L, 3669L, 3670L, 3671L, 3672L,
3673L, 3674L, 3675L, 3676L, 3677L, 3678L, 3679L, 3680L, 3681L,
3682L, 3683L, 3684L, 3685L, 3686L, 3687L, 3688L, 3689L, 3690L,
3691L, 3692L, 3693L, 3694L, 3695L, 3696L, 3697L, 3698L, 3699L,
3700L))
## ----ComBox1, echo=FALSE, result="hide", fig.cap="Boxplots for tail intensities in Comet assay.", message=FALSE, warning=FALSE, fig.pos="H", fig.height=4.5, fig.width=6.5, fig.align='center', dev="png",dpi=300----
boxplot(Tail.intensity ~ Treatment, data=TIComet, ylab="Tail Intensity", xlab="Dose")
points(y=TIComet$Tail.intensity, x=TIComet$Treatment, col=as.integer(TIComet$Animal_no))
## ----techn, echo=TRUE,results='asis', warning=FALSE, message=FALSE------------
TIComet$Dose[TIComet$Treatment=="VC"] <-0
TIComet$Dose[TIComet$Treatment=="Low"] <-25
TIComet$Dose[TIComet$Treatment=="medium"] <-75
TIComet$Dose[TIComet$Treatment=="High"] <-150
TIComet$Dose[TIComet$Treatment=="Superhigh"] <-500
TIComet$logTI <-log(TIComet$Tail.intensity+0.001)
mixL <- lmer(logTI ~ Dose+(1|Animal_no/Sample), data=TIComet)
EXB <- tukeytrendfit(mixL, dose="Dose", scaling=c("ari", "ord", "arilog"))
EXBB <- summary(glht(model=EXB$mmm, linfct=EXB$mlf))
## ----FENU, echo=FALSE, results='hide', warning=FALSE, message=FALSE-----------
fenu <-
structure(list(subject = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L,
9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 13L,
13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L,
18L, 18L, 18L), period = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), trt = c(4L,
8L, 0L, 4L, 0L, 8L, 8L, 4L, 0L, 0L, 4L, 8L, 8L, 0L, 4L, 0L, 8L,
4L, 0L, 4L, 8L, 0L, 8L, 4L, 8L, 4L, 0L, 4L, 0L, 8L, 4L, 8L, 0L,
8L, 0L, 4L, 8L, 4L, 0L, 4L, 8L, 0L, 8L, 0L, 4L, 0L, 4L, 8L, 4L,
0L, 8L, 0L, 8L, 4L), Ad_Lib_Lunch = c(1189.756, 576.962, 641.998,
1554.898, 1297.948, 1245.796, 1261.746, 1169.432, 1126.988, 1078.072,
1071.87, 825.067, 620.38, 1134.193, 1394.231, 727.288, 728.063,
986.057, 957.849, 1136.109, 957.413, 2156.761, 1656.231, 1769.438,
1206.818, 1204.818, 1385.801, 1011.428, 1345.279, 1173.188, 1173.106,
1066.703, 1137.53, 1745.437, 1136.524, 1630.337, 1035.507, 1568.084,
1786.647, 1327.06, 1219.183, 1276.848, 2068.996, 2616.345, 2161.583,
1363.187, 1379.031, 1676.081, 1398.99, 794.473, 526.105, 1157.478,
1240.915, 1207.228)), .Names = c("subject", "period", "trt",
"Ad_Lib_Lunch"), class = "data.frame", row.names = c(NA, -54L
))
## ----fenu2, echo=TRUE, warning=FALSE, message=FALSE---------------------------
library("lme4")
modFE <- lmer(Ad_Lib_Lunch ~ trt + period + (1| subject), REML = TRUE, data=fenu)
ttfe <- tukeytrendfit(modFE, dose="trt", scaling=c("ari", "ord", "arilog"), ddf="KR")
compfe <- summary(glht(model=ttfe$mmm, linfct=ttfe$mlf))
compfe
## ----fenucorr, echo=TRUE, warning=FALSE, message=FALSE------------------------
round(cov2cor(vcov(compfe)), digits=2)
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## ----warning=FALSE, message=FALSE---------------------------------------------
library("xtable")
## ----echo=TRUE, eval=FALSE, warning=FALSE, message=FALSE---------------------
# library("devtools")
# install_github("lahothorn/SiTuR")
## ----Call, echo=FALSE, warning=FALSE, message=FALSE---------------------------
library("tukeytrend")
library("multcomp")
## ----Exam1, echo=TRUE, results='hide', warning=FALSE, message=FALSE-----------
data(litter, package="multcomp")
dl <- litter
dl$dosen <- as.numeric(as.character(dl$dose))
fitw <- lm(weight ~ dosen, data=dl)
ttw <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"))
exa1<-summary(glht(model=ttw$mmm, linfct=ttw$mlf))
## ----Exam1a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa1 <- data.frame(names(exa1$test$tstat),exa1$test$tstat, exa1$test$pvalues)
colnames(Exa1) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa1, digits=4, caption="Adjusted p-values for standard Tukey Trend Test",
label="tab:exa1"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam1c, echo=FALSE, fig.cap='Mean litter weights', fig.align='center',fig.pos="H", fig.width=4.5, fig.height=4.0----
boxplot(weight~dose, data=dl)
points(y=dl$weight, x=dl$dose, pch=16)
## ----Exam2, echo=TRUE, warning=FALSE, message=FALSE---------------------------
ttdf <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa1a <- summary(glht(model=ttdf$mmm, linfct=ttdf$mlf, df=min(ttdf$df)))
## ----Exam3, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
fitc <- lm(weight ~ dosen + gesttime+number, data=dl)
covars<-anova(fitc)
ttc<- tukeytrendfit(fitc, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa3 <- summary(glht(model=ttc$mmm, linfct=ttc$mlf))
## ----Exam3a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa3 <- data.frame(names(exa3$test$tstat),exa3$test$tstat, exa3$test$pvalues)
colnames(Exa3) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa3, digits=4, caption="Adjusted p-values for finite Tukey Trend Test- adjusted against covariates",
label="tab:exa3"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam4data, echo=FALSE, results='hide', warning=FALSE, message=FALSE------
liv <-
structure(list(Dose = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62.5, 62.5,
62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 125, 125, 125,
125, 125, 125, 125, 125, 125, 125, 250, 250, 250, 250, 250, 250,
250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 500, 500, 500,
500, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000
), BodyWt = c(337.6, 319.2, 368.7, 373.3, 314.9, 306, 326.2,
331.7, 319.7, 304.9, 321, 325.9, 342.9, 322.6, 330.6, 302.2,
325.2, 326.7, 297.1, 329.8, 350.8, 305.5, 333.3, 348.2, 359.9,
345.7, 338, 322.2, 351.2, 309.8, 338, 337.3, 338.8, 329.7, 349.2,
298.4, 332.6, 323.3, 318.3, 330.6, 306.1, 320.3, 301.1, 320.7,
326.8, 313.4, 324.8, 315.2, 320.1, 308.2, 278, 297.8, 320.9,
313.4, 291.2, 293.8, 294.4, 280.9, 316.8, 292.2), LiverWt = c(10.73,
9.89, 12.6, 13.43, 10.21, 10.64, 10.76, 11.34, 10.55, 8.79, 9.27,
10.62, 11.53, 9.68, 10.53, 10.05, 10.09, 10.84, 8.87, 11.49,
12.02, 9.42, 12.55, 11.11, 13.67, 10.94, 11.66, 11.03, 11.85,
10.24, 10.7, 10.59, 9.89, 10.57, 10.84, 9.54, 11.35, 10.58, 11.09,
9.93, 9.2, 9.52, 9.58, 9.62, 9.89, 8.18, 9.41, 9.05, 8.56, 8.96,
7.92, 9.25, 9.38, 9.23, 9.02, 9.07, 9.1, 7.9, 9.48, 8.46)), .Names = c("Dose",
"BodyWt", "LiverWt"), row.names = c(NA, 60L), class = "data.frame")
## ----Exam4, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
#data("liv", package="SiTuR")
liv$relLiv <-liv$LiverWt/liv$BodyWt
mod1<-lm(LiverWt~Dose, data=liv)
mod2<-lm(relLiv~Dose, data=liv)
mod3<-lm(LiverWt~Dose+BodyWt, data=liv)
tt1<- tukeytrendfit(mod1, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
tt2<- tukeytrendfit(mod2, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
tt3<- tukeytrendfit(mod3, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
cttC <- combtt(tt1,tt2, tt3)
Exa4 <- summary(glht(model=cttC$mmm, linfct=cttC$mlf))
## ----Exam5data, echo=FALSE, results='hide', warning=FALSE, message=FALSE------
clin <-
structure(list(Dose = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62.5, 62.5,
62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 62.5, 125, 125, 125,
125, 125, 125, 125, 125, 125, 125, 250, 250, 250, 250, 250, 250,
250, 250, 250, 250, 500, 500, 500, 500, 500, 500, 500, 500, 500,
500, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000
), BUN = c(17.3, 15, 15.7, 16.5, 16.7, 15.2, 16.2, 16.6, 17.9,
16.7, 18.4, 21.1, 17.1, 18.6, 18.4, 19.1, 22.9, 22.2, 19.8, 20.4,
20, 18, 17.9, 17.6, 16.7, 16, 17.3, 17.2, 15.6, 15.5, 19.2, 21.1,
20.7, 19.8, 20.4, 16.9, 16.6, 18.2, 16.8, 16.5, 21.9, 19.2, 22.3,
18.6, 18.1, 21, 16.3, 20, 15.2, 17.8, 23.4, 21.9, 22.9, 21.2,
24.3, 17.4, 17.4, 18.7, 20.5, 19), Creat = c(0.6, 0.6, 0.5, 0.5,
0.5, 0.6, 0.6, 0.5, 0.6, 0.5, 0.5, 0.5, 0.4, 0.5, 0.5, 0.5, 0.6,
0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5,
0.5, 0.6, 0.5, 0.5, 0.6, 0.5, 0.5, 0.5, 0.6, 0.5, 0.5, 0.6, 0.6,
0.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0.5, 0.5, 0.5,
0.5, 0.5, 0.6, 0.5), `TP ` = c(7.4, 6.9, 7.1, 7.1, 7.2, 6.6,
7, 6.8, 6.8, 6.5, 6.3, 6.2, 6.5, 6.5, 7, 6.4, 6.5, 6.7, 6.5,
6.4, 7, 7.3, 6.9, 6.8, 7, 7.3, 7, 7, 6.7, 6.3, 6.7, 6.7, 6.2,
6.6, 6.6, 6.8, 7.1, 6.8, 6.4, 6.6, 6.5, 6.9, 6.3, 6.9, 6.4, 6.6,
6.4, 7.4, 6.5, 7.4, 6.1, 6.4, 6.4, 6.8, 6.3, 5.7, 6.2, 6.1, 6.1,
6.7), `ALB ` = c(5, 4.7, 4.9, 4.9, 5, 4.6, 4.8, 4.6, 4.6, 4.5,
4.4, 4.3, 4.4, 4.3, 4.7, 4.4, 4.6, 4.7, 4.4, 4.5, 4.9, 5, 4.7,
4.8, 4.8, 5, 4.8, 4.8, 4.6, 4.4, 4.6, 4.7, 4.5, 4.6, 4.6, 4.8,
5, 4.7, 4.5, 4.5, 4.5, 4.8, 4.4, 4.8, 4.6, 4.7, 4.6, 5.1, 4.6,
5.1, 4.3, 4.4, 4.5, 4.8, 4.4, 4, 4.3, 4.2, 4.2, 4.7), SerumGlucose = c(137,
180, 164, 145, 126, 126, 124, 127, 120, 144, 138, 151, 136, 133,
121, 141, 131, 125, 124, 141, 131, 140, 168, 131, 141, 125, 126,
143, 137, 196, 125, 175, 188, 144, 157, 139, 150, 144, 158, 137,
142, 158, 187, 119, 159, 160, 146, 119, 141, 126, 173, 184, 123,
127, 168, 142, 125, 141, 146, 113), CreatKinase = c(202, 205,
188, 155, 160, 229, 107, 101, 277, 343, 240, 276, 247, 164, 144,
1135, 131, 189, 250, 330, 336, 428, 239, 265, 239, 284, 165,
203, 224, 264, 384, 220, 206, 271, 241, 362, 295, 317, 429, 233,
369, 366, 462, 409, 237, 419, 268, 344, 297, 420, 403, 336, 330,
362, 497, 444, 401, 337, 838, 370), ALT = c(100, 56, 70, 54,
64, 64, 61, 77, 57, 41, 192, 413, 171, 288, 225, 497, 590, 553,
623, 821, 214, 343, 70, 158, 195, 261, 122, 230, 295, 287, 227,
182, 215, 279, 468, 233, 284, 268, 192, 105, 189, 247, 333, 162,
166, 132, 161, 330, 227, 511, 196, 268, 297, 249, 334, 182, 118,
222, 322, 288), SDH = c(35.1, 18.7, 18.4, 19.6, 21.5, 27.2, 23.8,
24.9, 16.4, 11, 42, 97.6, 35.6, 58.2, 49.4, 104, 142.8, 137.6,
146, 198.4, 73.6, 114.4, 14.8, 40.4, 52.8, 83.6, 33.7, 58.7,
104.4, 75.2, 78.4, 62, 67.2, 84.8, 176.4, 56.6, 100, 103.6, 50,
26, 80.8, 88.8, 130.8, 43.2, 52.8, 33.2, 53.9, 119.6, 101.2,
252, 76.8, 140, 122.4, 121.6, 140, 60.9, 29.6, 99.2, 133.6, 106
), ALP = c(159, 150, 155, 161, 167, 187, 155, 204, 170, 155,
173, 193, 178, 213, 180, 200, 187, 225, 211, 204, 120, 119, 122,
133, 123, 118, 137, 135, 115, 116, 105, 129, 104, 110, 128, 108,
122, 114, 105, 101, 116, 111, 101, 109, 119, 122, 108, 104, 116,
103, 104, 109, 132, 98, 101, 98, 116, 116, 101, 92), Nuc = c(41.1,
40.7, 39.3, 43.2, 43.3, 50.1, 41.8, 44.7, 44.5, 45, 39.6, 41.8,
35.7, 34.5, 35.7, 48.5, 41.1, 42.5, 44, 42.4, 38.8, 47.2, 39.1,
43.8, 39.3, 41.2, 41.7, 43.7, 42.8, 40, 38.5, 42, 37, 40.2, 45.4,
39.2, 43.7, 42.5, 40.2, 37.1, 39.9, 39.3, 40.2, 40, 38.3, 50.1,
43.3, 44, 53.8, 55.5, 44.2, 47, 44, 45.2, 53.1, 48.5, 49.1, 55.4,
51.6, 50.9), TBA = c(20.5, 39.7, 21.9, 20, 14.2, 14.1, 20.5,
16.5, 12.3, 17, 21.5, 38.2, 29.6, 49.8, 55.4, 38.4, 58.5, 69.4,
60.2, 83.3, 42.4, 37.9, 34.8, 28.8, 62.9, 51.4, 15.9, 37.5, 32.4,
55.3, 36.7, 22.8, 53.1, 47.8, 46.7, 22.6, 36, 28.6, 33.2, 25.2,
61.9, 33.5, 56.4, 67.7, 36.7, 32.9, 25.9, 35.8, 27.8, 73.9, 50,
44.1, 51, 35.8, 40, 31.3, 21.5, 33.8, 51, 28.6), Cholesterol = c(102,
100, 106, 95, 96, 80, 96, 88, 95, 93, 126, 127, 103, 112, 122,
107, 117, 102, 98, 97, 97, 101, 99, 83, 89, 97, 97, 96, 91, 90,
87, 93, 75, 84, 97, 83, 88, 79, 85, 95, 89, 93, 86, 83, 76, 79,
73, 91, 74, 90, 83, 86, 84, 84, 81, 64, 75, 70, 75, 86), Triglyceride = c(236,
102, 144, 130, 125, 93, 235, 67, 145, 108, 116, 172, 90, 154,
95, 131, 135, 107, 86, 70, 70, 167, 67, 81, 90, 114, 108, 95,
86, 104, 90, 84, 75, 86, 102, 81, 55, 95, 72, 67, 79, 66, 74,
100, 69, 51, 37, 98, 86, 101, 49, 49, 85, 80, 29, 55, 59, 32,
79, 70), Chloride = c(99, 101, 104, 103, 99, 101, 103, 102, 103,
106, 98, 101, 101, 99, 100, 102, 101, 101, 101, 103, 94, 102,
104, 101, 100, 102, 94, 103, 101, 103, 97, 102, 103, 102, 104,
104, 103, 103, 109, 104, 101, 102, 107, 102, 104, 107, 103, 101,
104, 102, 101, 103, 102, 100, 105, 106, 103, 104, 105, 105),
Sodium = c(146, 141, 144, 147, 146, 147, 150, 148, 149, 147,
145, 145, 145, 147, 148, 146, 147, 150, 147, 146, 139, 146,
147, 147, 146, 149, 138, 148, 147, 144, 144, 145, 145, 147,
150, 148, 148, 146, 150, 148, 145, 145, 147, 149, 150, 147,
149, 147, 149, 148, 144, 146, 147, 143, 145, 149, 147, 148,
144, 150), Calc = c(12.2, 11.4, 11.4, 11.3, 11.7, 11.4, 12.1,
11.4, 11.9, 11.7, 11.6, 11.1, 11, 11.5, 11.8, 11.2, 11.3,
11.8, 11.6, 11.7, 11.9, 11.6, 11.7, 11.6, 11.4, 11.9, 11.9,
12, 11.6, 12.2, 11.5, 11.5, 10.9, 11.3, 11.4, 11.3, 11.6,
11.4, 11.9, 11.8, 11.4, 11.4, 11.4, 11.8, 11.2, 11.5, 11.6,
12, 11.5, 12.2, 11.1, 11.5, 11.2, 11.6, 11.2, 11.2, 11.3,
11.2, 11.1, 11.6), PHOS = c(4.9, 5.6, 3.8, 2.9, 3.8, 5.2,
5.3, 5.5, 4.9, 5.6, 5.9, 3.7, 3.9, 5.6, 6.3, 5.6, 5.5, 7.7,
5.6, 7.1, 5.7, 3.1, 4.6, 5.4, 3.8, 4.6, 5.5, 5.1, 5.1, 7,
4.6, 3.9, 4.3, 3.8, 3.1, 4.1, 3.4, 2.6, 4.8, 6, 6.3, 3.3,
4.8, 4.4, 3.7, 5.3, 6.8, 5, 3.9, 4.4, 7.4, 5, 5.3, 5.6, 5.5,
4.8, 5.7, 4.9, 7.1, 3.7), Potassium = c(5, 3.7, 3.6, 4.4,
4.9, 5.1, 4.9, 5.5, 5.1, 4.9, 5.3, 4.6, 5, 5.3, 5.7, 5.2,
5.1, 5.4, 5.2, 4.9, 4.6, 4.9, 4.1, 5.2, 4.9, 5.3, 4.8, 5.6,
5.2, 5.1, 4.9, 4.8, 5, 5.1, 4.5, 5, 5.2, 4.8, 4.4, 5.5, 4.9,
4.4, 4.4, 5.2, 5, 4.7, 5.2, 5, 5.4, 5.5, 4.9, 4, 5.2, 5.2,
5.4, 5, 5.3, 5.5, 4.7, 5.4)), .Names = c("Dose", "BUN", "Creat",
"TP ", "ALB ", "SerumGlucose", "CreatKinase", "ALT", "SDH", "ALP",
"Nuc", "TBA", "Cholesterol", "Triglyceride", "Chloride", "Sodium",
"Calc", "PHOS", "Potassium"), row.names = c(NA, 60L), class = "data.frame")
## ----Exam5, echo=FALSE, fig.cap="Boxplots of creatin kinase", message=FALSE, warning=FALSE, fig.pos="H", fig.height=3.5,fig.width=4.5, fig.align='center'----
#data("clin", package="SiTuR")
library("reshape2")
mclin <- na.omit(melt(clin, id="Dose"))
mclinA <-droplevels(mclin[mclin$variable=="CreatKinase", ])
boxplot(value ~ Dose, data=mclinA, ylab="Creatine Kinase")
points(y=mclinA$value, x=factor(mclinA$Dose), pch=16)
## ----Exam5a, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitG<-lm(value~Dose, data=mclinA)
library("sandwich")
ttO <- tukeytrendfit(fitG, dose="Dose", scaling=c("ari", "ord", "arilog"), ddf="residual")
exa5a<-summary(glht(model=ttO$mmm, linfct=ttO$mlf))
exa5b<-summary(glht(model=ttO$mmm, linfct=ttO$mlf, vcov = sandwich))
## ----Examb, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
fitG<-lm(value~Dose, data=mclinA)
library("sandwich")
ttG <- tukeytrendfit(fitG, dose="Dose", scaling=c("ari", "ord", "arilog"),
ddf="residual", d0shift=c(0.2,0.5,1, 2) )
exa5c<-summary(glht(model=ttG$mmm, linfct=ttG$mlf, vcov = sandwich))
## ----Exam6, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw6 <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "log", "arilog", "highvslow"))
Exa6 <- summary(glht(model=ttw6$mmm, linfct=ttw6$mlf, alternative="less"))
## ----Exam8, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw8 <- tukeytrendfit(fitc, dose="dosen", ddf="residual",
scaling=c("ari", "ord", "log", "arilog", "treat"),
ctype="Williams")
exa8<-summary(glht(ttw8$mmm, ttw8$mlf, alternative="less"))
## ----Exam8a, echo=FALSE, results='asis', message=FALSE, warning=FALSE---------
Exa8 <- data.frame(names(exa8$test$tstat),exa8$test$tstat, exa8$test$pvalues)
colnames(Exa8) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exa8, digits=4, caption="Tukey and Williams Trend Test",
label="tab:exa8"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam9, echo=TRUE,results='hide', warning=FALSE, message=FALSE------------
ttw9 <- tukeytrendfit(fitw, dose="dosen",
scaling=c("ari", "ord", "log", "arilog", "treat"),
ctype="Dunnett")
Exa9<-summary(glht(ttw9$mmm, ttw9$mlf, alternative="less"))
## ----Exam10, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
tt10 <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog", "treat"),
ctype="UmbrellaWilliams", ddf="residual")
Exa10<-summary(glht(model=tt10$mmm, linfct=tt10$mlf, alternative="less"))
## ----Exam10a, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
dl$dos500<-dl$dosen; dl$dos50<-dl$dosen
dl$dos500[dl$dosen==500] <-NA
dl$dos50[dl$dosen==500] <-NA
dl$dos50[dl$dos50==50] <-NA
fitall<-lm(weight ~ dosen, data=dl)
fit500 <- lm(weight ~ dos500, data=dl)
fit50 <- lm(weight ~ dos50, data=dl)
tt50<- tukeytrendfit(fit50, dose="dos50", scaling=c("ari"), ddf="residual")
tt500<- tukeytrendfit(fit500, dose="dos500", scaling=c("ari", "ord", "arilog"), ddf="residual")
ttall<- tukeytrendfit(fitall, dose="dosen", scaling=c("ari", "ord", "arilog"), ddf="residual")
combi10 <- combtt(tt50,tt500,ttall)
comptt<- summary(glht(model=combi10$mmm, linfct=combi10$mlf, alternative="less"))
## ----pooledC, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
library("SimComp")
set.seed(170549)
d1 <- ermvnorm(n=73,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d2 <- ermvnorm(n=61,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d3 <- ermvnorm(n=75,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
d4 <- ermvnorm(n=77,mean=c(12.9, 22.0, 23.0, 24.4),sd=c(29.07,32.82, 30.99, 31.77))
dose <- as.numeric(rep(c(0, 1, 2, 4),c(73,61, 75, 77)))
IBDQ<-c(d1[,1], d2[,2], d3[,3], d4[,4])
rut<-data.frame(IBDQ,dose)
lmRU <-glm(IBDQ~dose, data=rut)
Cmat3 <-- c(-3, 1, 1, 1)
EXRU <- tukeytrendfit(lmRU, dose="dose", scaling=c("ari", "ord", "arilog", "treat"), ctype=Cmat3)
EXRRU <- summary(glht(model=EXRU$mmm, linfct=EXRU$mlf))
## ----poolC, echo=FALSE, results='asis', message=FALSE, warning=FALSE----------
Exrru <- data.frame(names(EXRRU$test$tstat),EXRRU$test$tstat, EXRRU$test$pvalues)
colnames(Exrru) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(Exrru, digits=4, caption="Tukey Trend Test and pooled dose vs. placebo test",
label="tab:exarru"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----weitere9a, echo=TRUE,results='asis', warning=FALSE, message=FALSE--------
verrier<-data.frame(
dose = c(5,10, 20, 40, 60),
events = c(28, 26,14,12,4),
n = c(70, 59, 90,88, 77))
lmV <-glm(cbind(events,n-events)~dose, data=verrier, family= binomial(link="logit"))
EXV <- tukeytrendfit(lmV, dose="dose", scaling=c("ari", "ord", "log"))
EXVV <- summary(glht(model=EXV$mmm, linfct=EXV$mlf))
## ----Exam11, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitLl <-lm(LiverWt~Dose, data=liv)
fitLb <-lm(BodyWt~Dose, data=liv)
ttLl <- tukeytrendfit(fitLl, dose="Dose", scaling=c("ari", "ord", "arilog"))
ttLb <- tukeytrendfit(fitLb, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttL <- combtt(ttLl, ttLb)
EXA11<-summary(glht(model=cttL$mmm, linfct=cttL$mlf))
## ----Exam11a, echo=FALSE, results='asis', message=FALSE, warning=FALSE--------
exa11 <- data.frame(names(EXA11$test$tstat), EXA11$test$tstat, EXA11$test$pvalues)
colnames(exa11) <-c("Dose metameter", "Test statistics", "$p$-value")
print(xtable(exa11, digits=4, caption="Bivariate Tukey Trend Test",
label="tab:ex11"),include.rownames=FALSE, caption.placement = "top",size="\\small",
sanitize.text.function=function(x){x})
## ----Exam15, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
# data("liv", package="SiTuR")
livv<-liv
livv[3,2] <- livv[14,2] <- livv[19,2] <- livv[46,2] <- livv[53,2] <- livv[54,2]<- NA
livv[1,3] <- livv[11,3] <- livv[18,3] <- livv[42,3] <- livv[55,3]<- NA #missing MCAR
fitLlv <-lm(LiverWt~Dose, data=livv)
fitLbv <-lm(BodyWt~Dose, data=livv)
ttLlv <- tukeytrendfit(fitLlv, dose="Dose", scaling=c("ari", "ord", "arilog"))
ttLbv <- tukeytrendfit(fitLbv, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttLv <- combtt(ttLlv, ttLbv)
Exa15<-summary(glht(model=cttLv$mmm, linfct=cttLv$mlf))
## ----mulBindata, echo=FALSE, results='hide', warning=FALSE, message=FALSE-----
miceF <-
structure(list(group = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3),
ID = c(230L, 236L, 208L, 231L, 244L, 229L, 234L, 210L, 201L,
205L, 228L, 207L, 246L, 209L, 219L, 202L, 217L, 235L, 227L,
203L, 212L, 213L, 222L, 232L, 247L, 248L, 204L, 206L, 211L,
214L, 215L, 216L, 218L, 220L, 221L, 223L, 224L, 225L, 226L,
233L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 245L, 249L,
250L, 251L, 263L, 268L, 279L, 267L, 289L, 280L, 275L, 277L,
296L, 276L, 253L, 284L, 272L, 298L, 261L, 285L, 278L, 291L,
290L, 264L, 281L, 292L, 295L, 270L, 297L, 256L, 255L, 265L,
299L, 252L, 283L, 254L, 266L, 274L, 257L, 258L, 259L, 260L,
262L, 269L, 271L, 273L, 282L, 286L, 287L, 288L, 293L, 294L,
300L, 344L, 335L, 342L, 314L, 312L, 303L, 308L, 315L, 318L,
320L, 313L, 334L, 339L, 302L, 307L, 323L, 310L, 305L, 306L,
338L, 331L, 309L, 319L, 321L, 337L, 326L, 348L, 322L, 330L,
346L, 332L, 304L, 301L, 311L, 347L, 349L, 316L, 317L, 324L,
325L, 327L, 328L, 329L, 333L, 336L, 340L, 341L, 343L, 345L,
350L, 396L, 397L, 386L, 354L, 385L, 352L, 356L, 384L, 369L,
361L, 381L, 351L, 390L, 377L, 376L, 367L, 353L, 363L, 370L,
394L, 366L, 362L, 383L, 365L, 364L, 357L, 392L, 382L, 388L,
389L, 393L, 371L, 358L, 398L, 355L, 374L, 379L, 373L, 399L,
380L, 391L, 395L, 378L, 360L, 368L, 400L, 359L, 375L, 372L,
387L), death = c(178, 564, 579, 610, 630, 632, 652, 663,
665, 665, 670, 673, 673, 695, 702, 706, 717, 718, 728, 731,
731, 731, 731, 731, 731, 731, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 477, 493, 516, 519, 562, 562,
567, 578, 582, 596, 604, 605, 605, 617, 620, 627, 629, 632,
633, 649, 652, 658, 661, 664, 675, 675, 687, 719, 720, 720,
721, 726, 731, 731, 731, 732, 732, 732, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 371, 465, 484, 489,
520, 521, 545, 582, 590, 597, 611, 619, 628, 633, 637, 641,
652, 658, 659, 659, 661, 690, 694, 694, 698, 704, 714, 717,
720, 722, 727, 728, 731, 731, 731, 731, 732, 732, 732, 732,
732, 732, 732, 732, 732, 732, 732, 732, 732, 732, 379, 450,
477, 505, 505, 518, 537, 548, 577, 588, 592, 596, 601, 603,
607, 615, 624, 626, 626, 627, 634, 652, 652, 654, 659, 662,
666, 676, 676, 676, 678, 679, 680, 681, 686, 686, 686, 687,
689, 692, 705, 711, 712, 717, 717, 718, 723, 730, 731, 731
), t01 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t02 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), t03 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0), t04 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t05 = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), t06 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), t07 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0), t08 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t09 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t11 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t12 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t13 = c(0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t14 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t15 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0), t16 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t17 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t18 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t19 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t20 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t21 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t22 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t23 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t24 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
1, 1, 0), t25 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1), t26 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,
0, 1, 0, 0, 1, 0, 0, 0, 0), t27 = c(0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,
1, 1, 0), t28 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,
1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0), t29 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 0, 1), t30 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t31 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t32 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t33 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t34 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t35 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t36 = c(0, 0, 0, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t37 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t38 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t39 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t40 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t41 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 1), t42 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1,
1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,
0, 0, 1), t43 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t44 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t45 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t46 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t47 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0), t48 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t49 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t50 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t51 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t52 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t53 = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t54 = c(0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t55 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t56 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t57 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t58 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t59 = c(0,
1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0,
0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0), t60 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t61 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t62 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t63 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t64 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t65 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t66 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t67 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t68 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t69 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t70 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t71 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t72 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t73 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t74 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t75 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t76 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t77 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t78 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t79 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t80 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t81 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t82 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t83 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t84 = c(0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t85 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t86 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), t87 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), t88 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), t89 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("group", "ID", "death",
"t01", "t02", "t03", "t04", "t05", "t06", "t07", "t08", "t09",
"t10", "t11", "t12", "t13", "t14", "t15", "t16", "t17", "t18",
"t19", "t20", "t21", "t22", "t23", "t24", "t25", "t26", "t27",
"t28", "t29", "t30", "t31", "t32", "t33", "t34", "t35", "t36",
"t37", "t38", "t39", "t40", "t41", "t42", "t43", "t44", "t45",
"t46", "t47", "t48", "t49", "t50", "t51", "t52", "t53", "t54",
"t55", "t56", "t57", "t58", "t59", "t60", "t61", "t62", "t63",
"t64", "t65", "t66", "t67", "t68", "t69", "t70", "t71", "t72",
"t73", "t74", "t75", "t76", "t77", "t78", "t79", "t80", "t81",
"t82", "t83", "t84", "t85", "t86", "t87", "t88", "t89"), row.names = c(NA,
200L), class = "data.frame")
## ----mulBin, echo=TRUE,results='asis', warning=FALSE, message=FALSE-----------
# data("miceF", package="SiTuR")
miceF$dose <- miceF$group
miceF$dose[miceF$dose==0] <-0
miceF$dose[miceF$dose==1] <-37
miceF$dose[miceF$dose==2] <-75
miceF$dose[miceF$dose==3] <-150
cst <- colSums(miceF[, 4:92])
tt5 <-as.matrix(miceF[, names(cst[cst>5])])
## ----mulBinFig, echo=FALSE, fig.cap='Presence/absence of 10 tumor classes in 200 mice', fig.pos="H", fig.width=6.5, fig.height=4.5----
tt5o <- cbind(tt5, "dose"=miceF$group+2)
ys <- (1:ncol(tt5o))/ncol(tt5o)
xs <- (1:nrow(tt5o))/nrow(tt5o)
par(bty="o", mar=c(2,4,4,1))
image(z=tt5o,y=ys, x=xs,
col=c("white", "black", "lightgrey", "lightblue", "blue", "darkblue"),
xaxt="n", yaxt="n", bty="o",
ylab="tumor", xlab="")
axis(side=2, at=ys, labels=colnames(tt5o),las=2)
axis(side=3, at=tapply(xs, miceF$dose, mean), labels=unique(miceF$dose),las=1, adj=0)
mtext(side=3, line=3, "dose group")
## ----mulBinMod, echo=TRUE, results='asis', warning=FALSE, message=FALSE-------
N24i <- glm(t24 ~ dose, data=miceF, family=binomial())
N26i <- glm(t26 ~ dose, data=miceF, family=binomial())
N27i <- glm(t27 ~ dose, data=miceF, family=binomial())
N28i <- glm(t28 ~ dose, data=miceF, family=binomial())
N29i <- glm(t29 ~ dose, data=miceF, family=binomial())
N36i <- glm(t36 ~ dose, data=miceF, family=binomial())
N41i <- glm(t41 ~ dose, data=miceF, family=binomial())
N42i <- glm(t42 ~ dose, data=miceF, family=binomial())
N59i <- glm(t59 ~ dose, data=miceF, family=binomial())
N71i <- glm(t71 ~ dose, data=miceF, family=binomial())
tu24i <- tukeytrendfit(N24i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu26i <- tukeytrendfit(N26i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu27i <- tukeytrendfit(N27i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu28i <- tukeytrendfit(N28i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu29i <- tukeytrendfit(N29i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu36i <- tukeytrendfit(N36i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu41i <- tukeytrendfit(N41i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu42i <- tukeytrendfit(N42i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu59i <- tukeytrendfit(N59i, dose="dose", scaling=c("ari", "ord", "arilog"))
tu71i <- tukeytrendfit(N71i, dose="dose", scaling=c("ari", "ord", "arilog"))
tt10 <- combtt(tu24i, tu26i, tu27i, tu28i, tu29i, tu36i, tu41i, tu42i, tu59i, tu71i)
stt10 <- summary(asglht(tt10))
## ----mulBin2, echo=FALSE, results='asis', warning=FALSE, message=FALSE--------
Tcar <- data.frame(names(stt10$test$tstat),stt10$test$tstat,stt10$test$pvalues)
colnames(Tcar)<-c("Model","Test stats", "p-value")
print(xtable(Tcar, digits=c(2,2,2,5), caption="Tukey trend test for bivariate binary",
label="tab:carc"), include.rownames=FALSE)
## ----Exam12, echo=TRUE,results='hide', warning=FALSE, message=FALSE-----------
fitw <- lm(weight ~ dosen + gesttime, data=dl) #lm-model
ttw <- tukeytrendfit(fitw, dose="dosen", scaling=c("ari", "ord", "arilog"))
fitqp <- glm(number~dosen + gesttime, data=dl, family=quasipoisson) # glm-model
ttqp <- tukeytrendfit(fitqp, dose="dosen", scaling=c("ari", "ord", "arilog"))
cttqw <- combtt(ttqp, ttw) # combine both models
Exa12<-summary(glht(model=cttqw$mmm, linfct=cttqw$mlf))
## ----pupEGData,echo=FALSE, warning=FALSE, message=FALSE-----------------------
wuleon <-
structure(list(Litter = c(60L, 60L, 60L, 60L, 60L, 60L, 60L,
61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
61L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L, 68L,
68L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 69L, 76L, 76L,
76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L, 76L,
76L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L, 77L,
77L, 77L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 84L, 85L,
85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 85L, 93L, 93L,
93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 100L, 100L, 100L,
100L, 100L, 100L, 100L, 100L, 101L, 101L, 101L, 101L, 101L, 101L,
106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L,
106L, 106L, 106L, 106L, 107L, 107L, 107L, 107L, 107L, 107L, 107L,
107L, 107L, 107L, 107L, 107L, 114L, 114L, 114L, 114L, 114L, 114L,
114L, 114L, 114L, 114L, 114L, 114L, 115L, 115L, 115L, 115L, 115L,
115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 122L, 122L, 122L,
122L, 122L, 122L, 122L, 122L, 122L, 122L, 123L, 123L, 123L, 123L,
123L, 123L, 123L, 123L, 123L, 123L, 130L, 130L, 130L, 130L, 130L,
130L, 130L, 130L, 130L, 130L, 130L, 131L, 131L, 131L, 131L, 131L,
131L, 131L, 131L, 131L, 131L, 131L, 131L, 138L, 138L, 138L, 138L,
138L, 138L, 138L, 138L, 138L, 138L, 138L, 138L, 138L, 139L, 139L,
139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L, 139L,
139L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L, 146L,
146L, 146L, 146L, 147L, 147L, 147L, 147L, 147L, 147L, 147L, 147L,
147L, 147L, 147L, 147L, 147L, 153L, 153L, 153L, 153L, 153L, 153L,
153L, 153L, 153L, 153L, 153L, 153L, 153L, 153L, 154L, 154L, 154L,
154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L, 154L,
54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 54L, 59L, 59L, 59L,
59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 59L, 62L, 62L,
62L, 62L, 62L, 62L, 62L, 62L, 62L, 62L, 62L, 67L, 67L, 67L, 67L,
67L, 67L, 67L, 67L, 67L, 67L, 67L, 67L, 70L, 70L, 70L, 70L, 70L,
70L, 70L, 70L, 70L, 70L, 70L, 70L, 75L, 75L, 75L, 75L, 75L, 75L,
75L, 75L, 75L, 75L, 75L, 75L, 75L, 78L, 78L, 78L, 78L, 78L, 78L,
78L, 78L, 78L, 78L, 78L, 78L, 78L, 78L, 78L, 86L, 86L, 86L, 86L,
86L, 86L, 86L, 86L, 86L, 86L, 86L, 91L, 91L, 91L, 91L, 91L, 91L,
91L, 91L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 94L,
99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 102L, 102L,
102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L,
102L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L,
116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L,
116L, 116L, 116L, 121L, 121L, 121L, 121L, 121L, 121L, 121L, 121L,
121L, 121L, 121L, 121L, 124L, 124L, 124L, 124L, 124L, 124L, 124L,
124L, 124L, 124L, 129L, 129L, 129L, 129L, 129L, 129L, 129L, 129L,
129L, 129L, 129L, 132L, 132L, 132L, 132L, 132L, 132L, 132L, 132L,
132L, 132L, 132L, 132L, 132L, 137L, 137L, 137L, 137L, 137L, 137L,
137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 140L, 140L,
140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L, 140L,
140L, 140L, 145L, 145L, 145L, 145L, 145L, 145L, 145L, 148L, 148L,
148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 152L,
152L, 152L, 152L, 152L, 152L, 152L, 155L, 155L, 155L, 155L, 155L,
155L, 155L, 155L, 155L, 55L, 55L, 55L, 55L, 55L, 55L, 55L, 55L,
55L, 55L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 63L, 66L,
66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 66L, 71L,
71L, 71L, 71L, 71L, 71L, 71L, 71L, 71L, 74L, 74L, 74L, 74L, 74L,
74L, 74L, 74L, 74L, 74L, 74L, 74L, 74L, 79L, 82L, 82L, 82L, 82L,
82L, 82L, 82L, 82L, 82L, 82L, 82L, 87L, 87L, 87L, 87L, 87L, 87L,
87L, 87L, 87L, 87L, 87L, 90L, 90L, 90L, 90L, 90L, 90L, 90L, 90L,
90L, 90L, 90L, 90L, 90L, 90L, 95L, 95L, 95L, 95L, 95L, 95L, 95L,
95L, 95L, 95L, 95L, 95L, 95L, 98L, 98L, 98L, 98L, 98L, 98L, 98L,
98L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 103L, 103L, 103L, 103L,
103L, 103L, 103L, 103L, 103L, 103L, 103L, 105L, 105L, 105L, 105L,
105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 112L, 112L, 112L,
112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 117L,
117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L, 117L,
117L, 125L, 125L, 125L, 125L, 125L, 125L, 125L, 128L, 128L, 128L,
128L, 128L, 128L, 128L, 128L, 136L, 136L, 136L, 141L, 141L, 141L,
141L, 141L, 141L, 141L, 144L, 144L, 144L, 144L, 144L, 144L, 144L,
144L, 144L, 144L, 149L, 149L, 149L, 149L, 149L, 149L, 149L, 149L,
149L, 149L, 149L, 149L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 57L,
57L, 57L, 57L, 57L, 57L, 57L, 57L, 57L, 57L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 65L, 65L, 65L, 65L, 65L,
65L, 65L, 65L, 65L, 65L, 65L, 72L, 72L, 72L, 72L, 72L, 72L, 72L,
72L, 72L, 72L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L,
80L, 80L, 80L, 80L, 81L, 81L, 81L, 81L, 81L, 81L, 81L, 89L, 89L,
89L, 89L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L,
96L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 97L,
104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L,
104L, 104L, 104L, 110L, 110L, 110L, 110L, 110L, 110L, 110L, 110L,
110L, 110L, 110L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L,
119L, 119L, 119L, 119L, 126L, 126L, 126L, 126L, 126L, 126L, 126L,
126L, 126L, 126L, 126L, 126L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 134L, 134L, 134L, 134L, 134L,
134L, 134L, 134L, 134L, 134L, 135L, 135L, 135L, 135L, 135L, 135L,
135L, 135L, 135L, 142L, 142L, 142L, 142L, 142L, 142L, 142L, 142L,
142L, 143L, 143L, 143L, 143L, 143L, 143L, 143L, 150L, 150L, 150L,
150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 150L, 151L, 151L,
151L, 151L, 151L, 151L, 151L, 156L, 156L, 156L, 156L, 156L, 156L,
156L, 156L, 156L, 156L, 156L, 156L), Dose = c(0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L, 750L,
750L, 750L, 750L, 750L, 750L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L, 1500L,
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L,
1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L,
1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("Litter",
"Dose", "Weight", "Malformation"), class = "data.frame", row.names = c(NA,
-1028L))
## ----pupEG0, echo=FALSE,results='asis'----------------------------------------
# data("wuleon", package="SiTuR")
wuleonTab<-xtable(wuleon, caption="Raw data of a fetal weight and malformation", label="tab:wu")
print(wuleonTab, include.rownames=FALSE, table.placement = "H",
add.to.row=list(pos=list(18:1018, 1019),
command=c("%", "$\\hdots$ & $\\hdots$ & $\\hdots$ & $\\hdots$ \\\\ \n")))
## ----pupEG, echo=FALSE, fig.cap='Ethylen glycol example', fig.pos="H", fig.width=6.5, fig.height=4.5, dev="png", dpi=300----
wuleon$Mal<- factor(wuleon$Malformation)
wuleon$Litter<- factor(wuleon$Litter)
boxplot(Weight~factor(Dose), data=wuleon)
points(y=wuleon$Weight, x=factor(wuleon$Dose), col=c("blue", "black")[wuleon$Mal])
## ----pupEGM, echo=TRUE, results='asis'----------------------------------------
wuleon$dose<-as.factor(wuleon$Dose)
fitwu <- lm(Weight~Dose, data=wuleon)
ttwu <- tukeytrendfit(fitwu, dose="Dose", scaling=c("ari", "ord", "arilog"))
fitwm <- glm(Malformation~Dose, family=binomial(),data=wuleon)
ttwm <- tukeytrendfit(fitwm, dose="Dose", scaling=c("ari", "ord", "arilog"))
cttwum <- combtt(ttwu, ttwm) # combine both models
Exawu<-summary(glht(model=cttwum$mmm, linfct=cttwum$mlf))
## ----pupEGM1, echo=FALSE, results='asis'--------------------------------------
Twu <- data.frame(names(Exawu$test$tstat),Exawu$test$tstat, Exawu$test$pvalues)
colnames(Twu)<-c("Model","Test stats", "p-value")
print(xtable(Twu, digits=c(2,2,2,7), caption="Tukey trend test for joint modeling of normal and binomial endpoint", label="tab:wuL"), include.rownames=FALSE)
## ----Exa16, echo=TRUE, results='asis', warning=FALSE, message=FALSE-----------
adverse <-c(rep("absent",11), rep("present",9), rep("absent",24), rep("present",19),
rep("absent",21),rep("present",21),rep("absent",21),rep("present",21),
rep("absent",17),rep("present",24))
#dose <-c(rep("0", 20), rep("0.125", 43),rep("0.5", 42),rep("0.75", 42), rep(1, 41))
dose <- c(rep(0, 20), rep(0.125, 43),rep(0.5, 42),rep(0.75, 42), rep(1, 41))
webster <- data.frame(adverse=factor(adverse),dose=dose)
t(table(webster))
lmW <-glm(adverse~dose, data=webster, family= binomial(link="logit"))
EX16 <- tukeytrendfit(lmW, dose="dose", scaling=c("ari", "ord", "arilog"))
EXA16 <- summary(glht(model=EX16$mmm, linfct=EX16$mlf))
## ----multRepDATA, echo=FALSE, fig.cap='Multiple overdispersed proportions.', message=FALSE, fig.pos="H", warning=FALSE----
if(requireNamespace("CorrBin")){
data(dehp, package="CorrBin")
Dehp <- dehp[rep(1:nrow(dehp),times=dehp$Freq),]
Dehp$p1<-Dehp$NResp.1/Dehp$ClusterSize
Dehp$p2<-Dehp$NResp.2/Dehp$ClusterSize
Dehp$p3<-Dehp$NResp.3/Dehp$ClusterSize
Dehp$Dose<-as.numeric(paste(Dehp$Trt))
MmN <- glm(cbind(NResp.1, ClusterSize-NResp.1)~Dose, data=Dehp, family= quasibinomial(link="logit"))
DmN <- glm(cbind(NResp.2, ClusterSize-NResp.2)~Dose, data=Dehp, family= quasibinomial(link="logit"))
NmN <- glm(cbind(NResp.3, ClusterSize-NResp.3)~Dose, data=Dehp, family= quasibinomial(link="logit"))
tMmN <- tukeytrendfit(MmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
tDmN <- tukeytrendfit(DmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
tNmN <- tukeytrendfit(NmN, dose="Dose", scaling=c("ari", "ord", "arilog"))
ctMDN <- combtt(tMmN, tDmN, tNmN)
Exa18<-summary(glht(model=ctMDN$mmm, linfct=ctMDN$mlf))
Exa18
}
## ----Exa20data, echo=FALSE,results='hide', warning=FALSE, message=FALSE-------
mn <- structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L), .Label = c("Control", "D188", "D375", "D750", "Positive"
), class = "factor"), animal = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 22, 23, 24),
MN = c(4, 2, 4, 2, 2, 3, 5, 7, 2, 0, 5, 6, 1, 4, 2, 2, 4,
1, 1, 0, 26, 28, 22, 37, 29)), .Names = c("group", "animal",
"MN"), row.names = c(NA, 25L), class = "data.frame")
## ----Exa20, echo=TRUE,results='asis', warning=FALSE, message=FALSE------------
#data("mn", package="SiTuR")
Mn<-droplevels(mn[mn$group != "Positive", ])
Mn$dose<-c(rep(0,5), rep(188, 5), rep(375,5), rep(750,5))
fitMN <- glm(MN ~ dose, data=Mn, family=quasipoisson(link="log"))
Exa20 <- tukeytrendfit(fitMN, dose="dose", scaling=c("ari", "ord", "arilog", "treat"),
ctype="UmbrellaWilliams")
EXA20<-summary(glht(model=Exa20$mmm, linfct=Exa20$mlf))
## ----OrderedDATA, echo=FALSE, results='hide', warning=FALSE, message=FALSE----
hyalin <-
structure(list(dose = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L), .Label = c("0", "5", "10",
"15", "20", "30", "45", "90"), class = "factor"), droplets = c(1,
1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 4, 4, 3, 3, 3, 4, 4, 4, 3,
3, 3, 4, 4, 4)), .Names = c("dose", "droplets"), class = "data.frame", row.names = c(NA,
48L))
## ----Ordered, echo=TRUE, results='asis', warning=FALSE, message=FALSE---------
#data("hyalin", package="SiTuR")
exP<-glm(droplets~dose, data=hyalin, family=quasipoisson(link = "log"))
ntab <- table(hyalin$dose) # sample sizes needed for unbalanced designs
cmd <- contrMat(n=ntab, type="Dunnett")
cmuw <- contrMat(n=ntab, type="UmbrellaWilliams")
cmduw <- rbind(cmd, cmuw) # combine both matrices
aa<-summary(glht(exP, linfct=mcp(dose=cmduw),alternative="greater"))
DlevelO<-as.numeric(levels(hyalin$dose))
S0O<-log(DlevelO[2])-log(DlevelO[3]/DlevelO[2])*(DlevelO[2]-DlevelO[1])/(DlevelO[3]-DlevelO[2])
hyalin$DoseN<-as.numeric(as.character(hyalin$dose))
hyalin$DoseO<-as.numeric(hyalin$dose)
hyalin$DoseL<-log(hyalin$DoseN)
hyalin$DoseLL<-hyalin$DoseL
hyalin$DoseLL[hyalin$DoseN==DlevelO[1]] <-S0O
# umbrella needed?
lmN <-glm(droplets~DoseN, data=hyalin,family=quasipoisson(link = "log"))
lmO <-glm(droplets~DoseO, data=hyalin, family=quasipoisson(link = "log"))
lmLL <-glm(droplets~DoseLL, data=hyalin, family=quasipoisson(link = "log"))
ordC <- summary(glht(mmm(covariate=lmN, ordinal=lmO, linlog=lmLL),
mlf(covariate="DoseN=0", ordinal="DoseO=0", linlog="DoseLL=0")))
## ----Ordered1, echo=FALSE,results='asis', warning=FALSE, message=FALSE--------
Tord <- data.frame(names(ordC$test$tstat),ordC$test$tstat, ordC$test$pvalues)
colnames(Tord)<-c("Model","Test stats", "p-value")
print(xtable(Tord, digits=7, caption="Tukey trend test for ordered categorical data",
label="tab:order"), include.rownames=FALSE)
## ----weitere7, echo=TRUE, results='asis', warning=FALSE, message=FALSE--------
if(requireNamespace("HSAUR3")){
data(bp, package="HSAUR3")
bp$Dose<-10^(bp$logdose)
fitbp <-lm(bloodp~Dose+recovtime, data=bp)
ttbp <- tukeytrendfit(fitbp, dose="Dose", scaling=c("ari", "ord", "log"))
EXA31 <- summary(glht(model=ttbp$mmm, linfct=ttbp$mlf))
EXA31}
## ----Exam21a, echo=TRUE,results='hide', warning=FALSE, message=FALSE----------
if(requireNamespace("SASmixed")){
data(AvgDailyGain, package="SASmixed")
AVG<-AvgDailyGain
library("lme4")
mmNN <-lmer(adg~Trt+InitWt+(1|Block), data=AVG)
tt21 <- tukeytrendfit(mmNN, dose="Trt", scaling=c("ari", "ord", "arilog"),
ddf="residual", d0shift=c(0.1, 0.5, 1, 5) )
comptt21 <- glht(model=tt21$mmm, linfct=tt21$mlf)
Exa21a<-summary(comptt21)
Exa21a
}
## ----atladat, echo=FALSE,results='asis', warning=FALSE, message=FALSE---------
atla <-
structure(list(dose = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L), centre = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L), weight = c(10.2, 8.8, 10.4, 9, 8.9, 8.4, 10.7, 9.3, 9.1,
8.7, 11.6, 9.9, 9.92, 9.5, 9.9, 9.9, 9.7, 10, 10.6, 8.2, 12.6,
11.4, 11.9, 12.1, 10.5, 12.8, 11.8, 10.3, 12.4, 12.6, 10.6, 13.1,
15.6, 12.6, 13.1, 13, 12.9, 14.9, 14.4, 12.5, 6.23, 5.99, 6.3,
5.49, 5.15, 6.03, 6.72, 5.8, 6.12, 5.82, 7.19, 5.83, 6.75, 5.98,
6.38, 6.36, 6.55, 6.82, 5.56, 7.13, 9.45, 7.53, 8.81, 8.06, 9.83,
8.98, 7.653, 8.99, 8.82, 8.79, 10.27, 10.67, 10.73, 9.46, 8.94,
10.9, 10.95, 10.93, 10.36, 9.96, 7.93, 9.14, 7.58, 9.14, 7.33,
8.05, 7.25, 7.11, 6.86, 7.09, 7.31, 8.09, 7.65, 7.27, 7.14, 7.37,
8.8, 7.87, 8.25, 7.22, 8.89, 8.46, 8.43, 9.37, 9.68, 10.27, 9.06,
9.74, 8.37, 9.18, 12.48, 12.16, 11.39, 10.66, 11.98, 13.66, 11.23,
12.98, 10.72, 11.23, 7.16, 5.9, 5.62, 7.02, 6.84, 6.12, 6.42,
6.03, 6.29, 7.36, 6.99, 6.45, 7.73, 6.33, 7.15, 6.91, 6.09, 7.35,
6.92, 10.02, 9.09, 9.15, 7.82, 8.94, 8.45, 6.84, 10.21, 11.62,
8.51, 11.14, 11.77, 10.11, 10.2, 11.25, 9.92, 10.54, 10.68, 11.464,
10.39, 6.7, 5.9, 5.7, 6.3, 6.9, 5.4, 5.7, 6.5, 5.9, 5.8, 6.5,
6.5, 6.6, 5.7, 5.6, 5.8, 7.2, 6.8, 6.8, 6.2, 9, 8.7, 7.7, 8.3,
8.6, 8, 7.8, 9.3, 8, 8.4, 10.7, 11.6, 8.5, 9.7, 11, 11.8, 11.9,
11, 10.5, 11.3, 7.09, 7.99, 8.02, 6.75, 7.2, 6.88, 7.16, 8.05,
7.77, 7.51, 8.11, 7.64, 6.59, 6.87, 8.02, 6.08, 7.3, 8.79, 8.1,
7.86, 9.99, 9.4, 8.88, 8.22, 9.54, 9.01, 9.69, 8.55, 8.77, 7.77,
12.55, 12.43, 9.67, 10.66, 10.94, 12.83, 11.33, 7.1, 10.12, 9.34,
9.52, 8.75, 8.88, 9.15, 10.43, 9.63, 7.81, 8.69, 9.08, 11.23,
8.19, 9.11, 8.07, 8.65, 8.08, 9.51, 8.59, 11.16, 12.82, 11.69,
10.4, 11.29, 12.36, 11.91, 12.46, 11.21, 10.22, 16.56, 11.87,
11.56, 13.17, 11.56, 14.06, 12.22, 11.36, 12.2, 13.2, 6.65, 5.68,
6.8, 6.61, 5.34, 6.11, 5.87, 6.66, 6.25, 6.4, 6.67, 6.06, 6.71,
6.83, 5.98, 5.61, 6.12, 6.96, 6.68, 7.43, 8.67, 8.01, 7.71, 7.94,
7.38, 8.28, 8.68, 8.15, 6.82, 6.65, 8.33, 7.6, 9.08, 9.74, 7.66,
8.51, 8.41, 9.36, 9.86, 8.75)), .Names = c("dose", "centre",
"weight"), class = "data.frame", row.names = c(NA, -316L))
atla$Dose[atla$dose==4] <-100
atla$Dose[atla$dose==3] <-30
atla$Dose[atla$dose==2] <-3
atla$Dose[atla$dose==1] <-0
atla$DOSE<-as.factor(atla$Dose)
## ----weitere2a, echo=TRUE,results='asis', warning=FALSE, message=FALSE--------
library("lme4")
mixedA <- lmer(weight ~ Dose+ (1|centre), data=atla)
tt24 <- tukeytrendfit(mixedA, dose="Dose", scaling=c("ari", "ord", "arilog"),
ddf="KR")
comptt24 <- glht(model=tt24$mmm, linfct=tt24$mlf)
Exa24<-summary(comptt24)
## ----weitere2p, echo=FALSE,results='asis', warning=FALSE, message=FALSE-------
exa24 <- data.frame(names(Exa24$test$tstat),Exa24$test$tstat, Exa24$test$pvalues)
colnames(exa24)<-c("Comparison","Test stats", "p-value")
print(xtable(exa24, digits=c(2,2,2,8), caption="Multicentre trial: mixed model",
label="tab:exa24"), include.rownames=FALSE)
## ----ComBox1DATA, echo=FALSE, results='hide',message=FALSE, warning=FALSE-----
TIComet <-
structure(list(Sample = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), Treatment = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L,
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L,
96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L,
57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L,
69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L,
81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L,
93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L,
54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L,
66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L,
78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L,
90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L,
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L,
63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L,
87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L,
99L, 100L), .Label = c("1", "2", "3", "4", "5", "6", "7",
"8", "9", "10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "23", "24", "25", "26", "27",
"28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47",
"48", "49", "50", "51", "52", "53", "54", "55", "56", "57",
"58", "59", "60", "61", "62", "63", "64", "65", "66", "67",
"68", "69", "70", "71", "72", "73", "74", "75", "76", "77",
"78", "79", "80", "81", "82", "83", "84", "85", "86", "87",
"88", "89", "90", "91", "92", "93", "94", "95", "96", "97",
"98", "99", "100"), class = "factor"), Animal_no = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
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393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 451L, 452L, 453L,
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465L, 466L, 467L, 468L, 469L, 470L, 471L, 472L, 473L, 474L, 475L,
476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L, 486L,
487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L, 497L,
498L, 499L, 500L, 501L, 502L, 503L, 504L, 505L, 506L, 507L, 508L,
509L, 510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L,
520L, 521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L,
531L, 532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L,
542L, 543L, 544L, 545L, 546L, 547L, 548L, 549L, 550L, 601L, 602L,
603L, 604L, 605L, 606L, 607L, 608L, 609L, 610L, 611L, 612L, 613L,
614L, 615L, 616L, 617L, 618L, 619L, 620L, 621L, 622L, 623L, 624L,
625L, 626L, 627L, 628L, 629L, 630L, 631L, 632L, 633L, 634L, 635L,
636L, 637L, 638L, 639L, 640L, 641L, 642L, 643L, 644L, 645L, 646L,
647L, 648L, 649L, 650L, 651L, 652L, 653L, 654L, 655L, 656L, 657L,
658L, 659L, 660L, 661L, 662L, 663L, 664L, 665L, 666L, 667L, 668L,
669L, 670L, 671L, 672L, 673L, 674L, 675L, 676L, 677L, 678L, 679L,
680L, 681L, 682L, 683L, 684L, 685L, 686L, 687L, 688L, 689L, 690L,
691L, 692L, 693L, 694L, 695L, 696L, 697L, 698L, 699L, 700L, 751L,
752L, 753L, 754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L,
763L, 764L, 765L, 766L, 767L, 768L, 769L, 770L, 771L, 772L, 773L,
774L, 775L, 776L, 777L, 778L, 779L, 780L, 781L, 782L, 783L, 784L,
785L, 786L, 787L, 788L, 789L, 790L, 791L, 792L, 793L, 794L, 795L,
796L, 797L, 798L, 799L, 800L, 801L, 802L, 803L, 804L, 805L, 806L,
807L, 808L, 809L, 810L, 811L, 812L, 813L, 814L, 815L, 816L, 817L,
818L, 819L, 820L, 821L, 822L, 823L, 824L, 825L, 826L, 827L, 828L,
829L, 830L, 831L, 832L, 833L, 834L, 835L, 836L, 837L, 838L, 839L,
840L, 841L, 842L, 843L, 844L, 845L, 846L, 847L, 848L, 849L, 850L,
901L, 902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L, 911L,
912L, 913L, 914L, 915L, 916L, 917L, 918L, 919L, 920L, 921L, 922L,
923L, 924L, 925L, 926L, 927L, 928L, 929L, 930L, 931L, 932L, 933L,
934L, 935L, 936L, 937L, 938L, 939L, 940L, 941L, 942L, 943L, 944L,
945L, 946L, 947L, 948L, 949L, 950L, 951L, 952L, 953L, 954L, 955L,
956L, 957L, 958L, 959L, 960L, 961L, 962L, 963L, 964L, 965L, 966L,
967L, 968L, 969L, 970L, 971L, 972L, 973L, 974L, 975L, 976L, 977L,
978L, 979L, 980L, 981L, 982L, 983L, 984L, 985L, 986L, 987L, 988L,
989L, 990L, 991L, 992L, 993L, 994L, 995L, 996L, 997L, 998L, 999L,
1000L, 1051L, 1052L, 1053L, 1054L, 1055L, 1056L, 1057L, 1058L,
1059L, 1060L, 1061L, 1062L, 1063L, 1064L, 1065L, 1066L, 1067L,
1068L, 1069L, 1070L, 1071L, 1072L, 1073L, 1074L, 1075L, 1076L,
1077L, 1078L, 1079L, 1080L, 1081L, 1082L, 1083L, 1084L, 1085L,
1086L, 1087L, 1088L, 1089L, 1090L, 1091L, 1092L, 1093L, 1094L,
1095L, 1096L, 1097L, 1098L, 1099L, 1100L, 1101L, 1102L, 1103L,
1104L, 1105L, 1106L, 1107L, 1108L, 1109L, 1110L, 1111L, 1112L,
1113L, 1114L, 1115L, 1116L, 1117L, 1118L, 1119L, 1120L, 1121L,
1122L, 1123L, 1124L, 1125L, 1126L, 1127L, 1128L, 1129L, 1130L,
1131L, 1132L, 1133L, 1134L, 1135L, 1136L, 1137L, 1138L, 1139L,
1140L, 1141L, 1142L, 1143L, 1144L, 1145L, 1146L, 1147L, 1148L,
1149L, 1150L, 1201L, 1202L, 1203L, 1204L, 1205L, 1206L, 1207L,
1208L, 1209L, 1210L, 1211L, 1212L, 1213L, 1214L, 1215L, 1216L,
1217L, 1218L, 1219L, 1220L, 1221L, 1222L, 1223L, 1224L, 1225L,
1226L, 1227L, 1228L, 1229L, 1230L, 1231L, 1232L, 1233L, 1234L,
1235L, 1236L, 1237L, 1238L, 1239L, 1240L, 1241L, 1242L, 1243L,
1244L, 1245L, 1246L, 1247L, 1248L, 1249L, 1250L, 1251L, 1252L,
1253L, 1254L, 1255L, 1256L, 1257L, 1258L, 1259L, 1260L, 1261L,
1262L, 1263L, 1264L, 1265L, 1266L, 1267L, 1268L, 1269L, 1270L,
1271L, 1272L, 1273L, 1274L, 1275L, 1276L, 1277L, 1278L, 1279L,
1280L, 1281L, 1282L, 1283L, 1284L, 1285L, 1286L, 1287L, 1288L,
1289L, 1290L, 1291L, 1292L, 1293L, 1294L, 1295L, 1296L, 1297L,
1298L, 1299L, 1300L, 1351L, 1352L, 1353L, 1354L, 1355L, 1356L,
1357L, 1358L, 1359L, 1360L, 1361L, 1362L, 1363L, 1364L, 1365L,
1366L, 1367L, 1368L, 1369L, 1370L, 1371L, 1372L, 1373L, 1374L,
1375L, 1376L, 1377L, 1378L, 1379L, 1380L, 1381L, 1382L, 1383L,
1384L, 1385L, 1386L, 1387L, 1388L, 1389L, 1390L, 1391L, 1392L,
1393L, 1394L, 1395L, 1396L, 1397L, 1398L, 1399L, 1400L, 1401L,
1402L, 1403L, 1404L, 1405L, 1406L, 1407L, 1408L, 1409L, 1410L,
1411L, 1412L, 1413L, 1414L, 1415L, 1416L, 1417L, 1418L, 1419L,
1420L, 1421L, 1422L, 1423L, 1424L, 1425L, 1426L, 1427L, 1428L,
1429L, 1430L, 1431L, 1432L, 1433L, 1434L, 1435L, 1436L, 1437L,
1438L, 1439L, 1440L, 1441L, 1442L, 1443L, 1444L, 1445L, 1446L,
1447L, 1448L, 1449L, 1450L, 1501L, 1502L, 1503L, 1504L, 1505L,
1506L, 1507L, 1508L, 1509L, 1510L, 1511L, 1512L, 1513L, 1514L,
1515L, 1516L, 1517L, 1518L, 1519L, 1520L, 1521L, 1522L, 1523L,
1524L, 1525L, 1526L, 1527L, 1528L, 1529L, 1530L, 1531L, 1532L,
1533L, 1534L, 1535L, 1536L, 1537L, 1538L, 1539L, 1540L, 1541L,
1542L, 1543L, 1544L, 1545L, 1546L, 1547L, 1548L, 1549L, 1550L,
1551L, 1552L, 1553L, 1554L, 1555L, 1556L, 1557L, 1558L, 1559L,
1560L, 1561L, 1562L, 1563L, 1564L, 1565L, 1566L, 1567L, 1568L,
1569L, 1570L, 1571L, 1572L, 1573L, 1574L, 1575L, 1576L, 1577L,
1578L, 1579L, 1580L, 1581L, 1582L, 1583L, 1584L, 1585L, 1586L,
1587L, 1588L, 1589L, 1590L, 1591L, 1592L, 1593L, 1594L, 1595L,
1596L, 1597L, 1598L, 1599L, 1600L, 1651L, 1652L, 1653L, 1654L,
1655L, 1656L, 1657L, 1658L, 1659L, 1660L, 1661L, 1662L, 1663L,
1664L, 1665L, 1666L, 1667L, 1668L, 1669L, 1670L, 1671L, 1672L,
1673L, 1674L, 1675L, 1676L, 1677L, 1678L, 1679L, 1680L, 1681L,
1682L, 1683L, 1684L, 1685L, 1686L, 1687L, 1688L, 1689L, 1690L,
1691L, 1692L, 1693L, 1694L, 1695L, 1696L, 1697L, 1698L, 1699L,
1700L, 1701L, 1702L, 1703L, 1704L, 1705L, 1706L, 1707L, 1708L,
1709L, 1710L, 1711L, 1712L, 1713L, 1714L, 1715L, 1716L, 1717L,
1718L, 1719L, 1720L, 1721L, 1722L, 1723L, 1724L, 1725L, 1726L,
1727L, 1728L, 1729L, 1730L, 1731L, 1732L, 1733L, 1734L, 1735L,
1736L, 1737L, 1738L, 1739L, 1740L, 1741L, 1742L, 1743L, 1744L,
1745L, 1746L, 1747L, 1748L, 1749L, 1750L, 1801L, 1802L, 1803L,
1804L, 1805L, 1806L, 1807L, 1808L, 1809L, 1810L, 1811L, 1812L,
1813L, 1814L, 1815L, 1816L, 1817L, 1818L, 1819L, 1820L, 1821L,
1822L, 1823L, 1824L, 1825L, 1826L, 1827L, 1828L, 1829L, 1830L,
1831L, 1832L, 1833L, 1834L, 1835L, 1836L, 1837L, 1838L, 1839L,
1840L, 1841L, 1842L, 1843L, 1844L, 1845L, 1846L, 1847L, 1848L,
1849L, 1850L, 1851L, 1852L, 1853L, 1854L, 1855L, 1856L, 1857L,
1858L, 1859L, 1860L, 1861L, 1862L, 1863L, 1864L, 1865L, 1866L,
1867L, 1868L, 1869L, 1870L, 1871L, 1872L, 1873L, 1874L, 1875L,
1876L, 1877L, 1878L, 1879L, 1880L, 1881L, 1882L, 1883L, 1884L,
1885L, 1886L, 1887L, 1888L, 1889L, 1890L, 1891L, 1892L, 1893L,
1894L, 1895L, 1896L, 1897L, 1898L, 1899L, 1900L, 1951L, 1952L,
1953L, 1954L, 1955L, 1956L, 1957L, 1958L, 1959L, 1960L, 1961L,
1962L, 1963L, 1964L, 1965L, 1966L, 1967L, 1968L, 1969L, 1970L,
1971L, 1972L, 1973L, 1974L, 1975L, 1976L, 1977L, 1978L, 1979L,
1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L,
1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L,
1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L,
2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L, 2014L, 2015L,
2016L, 2017L, 2018L, 2019L, 2020L, 2021L, 2022L, 2023L, 2024L,
2025L, 2026L, 2027L, 2028L, 2029L, 2030L, 2031L, 2032L, 2033L,
2034L, 2035L, 2036L, 2037L, 2038L, 2039L, 2040L, 2041L, 2042L,
2043L, 2044L, 2045L, 2046L, 2047L, 2048L, 2049L, 2050L, 2101L,
2102L, 2103L, 2104L, 2105L, 2106L, 2107L, 2108L, 2109L, 2110L,
2111L, 2112L, 2113L, 2114L, 2115L, 2116L, 2117L, 2118L, 2119L,
2120L, 2121L, 2122L, 2123L, 2124L, 2125L, 2126L, 2127L, 2128L,
2129L, 2130L, 2131L, 2132L, 2133L, 2134L, 2135L, 2136L, 2137L,
2138L, 2139L, 2140L, 2141L, 2142L, 2143L, 2144L, 2145L, 2146L,
2147L, 2148L, 2149L, 2150L, 2151L, 2152L, 2153L, 2154L, 2155L,
2156L, 2157L, 2158L, 2159L, 2160L, 2161L, 2162L, 2163L, 2164L,
2165L, 2166L, 2167L, 2168L, 2169L, 2170L, 2171L, 2172L, 2173L,
2174L, 2175L, 2176L, 2177L, 2178L, 2179L, 2180L, 2181L, 2182L,
2183L, 2184L, 2185L, 2186L, 2187L, 2188L, 2189L, 2190L, 2191L,
2192L, 2193L, 2194L, 2195L, 2196L, 2197L, 2198L, 2199L, 2200L,
2251L, 2252L, 2253L, 2254L, 2255L, 2256L, 2257L, 2258L, 2259L,
2260L, 2261L, 2262L, 2263L, 2264L, 2265L, 2266L, 2267L, 2268L,
2269L, 2270L, 2271L, 2272L, 2273L, 2274L, 2275L, 2276L, 2277L,
2278L, 2279L, 2280L, 2281L, 2282L, 2283L, 2284L, 2285L, 2286L,
2287L, 2288L, 2289L, 2290L, 2291L, 2292L, 2293L, 2294L, 2295L,
2296L, 2297L, 2298L, 2299L, 2300L, 2301L, 2302L, 2303L, 2304L,
2305L, 2306L, 2307L, 2308L, 2309L, 2310L, 2311L, 2312L, 2313L,
2314L, 2315L, 2316L, 2317L, 2318L, 2319L, 2320L, 2321L, 2322L,
2323L, 2324L, 2325L, 2326L, 2327L, 2328L, 2329L, 2330L, 2331L,
2332L, 2333L, 2334L, 2335L, 2336L, 2337L, 2338L, 2339L, 2340L,
2341L, 2342L, 2343L, 2344L, 2345L, 2346L, 2347L, 2348L, 2349L,
2350L, 2401L, 2402L, 2403L, 2404L, 2405L, 2406L, 2407L, 2408L,
2409L, 2410L, 2411L, 2412L, 2413L, 2414L, 2415L, 2416L, 2417L,
2418L, 2419L, 2420L, 2421L, 2422L, 2423L, 2424L, 2425L, 2426L,
2427L, 2428L, 2429L, 2430L, 2431L, 2432L, 2433L, 2434L, 2435L,
2436L, 2437L, 2438L, 2439L, 2440L, 2441L, 2442L, 2443L, 2444L,
2445L, 2446L, 2447L, 2448L, 2449L, 2450L, 2451L, 2452L, 2453L,
2454L, 2455L, 2456L, 2457L, 2458L, 2459L, 2460L, 2461L, 2462L,
2463L, 2464L, 2465L, 2466L, 2467L, 2468L, 2469L, 2470L, 2471L,
2472L, 2473L, 2474L, 2475L, 2476L, 2477L, 2478L, 2479L, 2480L,
2481L, 2482L, 2483L, 2484L, 2485L, 2486L, 2487L, 2488L, 2489L,
2490L, 2491L, 2492L, 2493L, 2494L, 2495L, 2496L, 2497L, 2498L,
2499L, 2500L, 2551L, 2552L, 2553L, 2554L, 2555L, 2556L, 2557L,
2558L, 2559L, 2560L, 2561L, 2562L, 2563L, 2564L, 2565L, 2566L,
2567L, 2568L, 2569L, 2570L, 2571L, 2572L, 2573L, 2574L, 2575L,
2576L, 2577L, 2578L, 2579L, 2580L, 2581L, 2582L, 2583L, 2584L,
2585L, 2586L, 2587L, 2588L, 2589L, 2590L, 2591L, 2592L, 2593L,
2594L, 2595L, 2596L, 2597L, 2598L, 2599L, 2600L, 2601L, 2602L,
2603L, 2604L, 2605L, 2606L, 2607L, 2608L, 2609L, 2610L, 2611L,
2612L, 2613L, 2614L, 2615L, 2616L, 2617L, 2618L, 2619L, 2620L,
2621L, 2622L, 2623L, 2624L, 2625L, 2626L, 2627L, 2628L, 2629L,
2630L, 2631L, 2632L, 2633L, 2634L, 2635L, 2636L, 2637L, 2638L,
2639L, 2640L, 2641L, 2642L, 2643L, 2644L, 2645L, 2646L, 2647L,
2648L, 2649L, 2650L, 2701L, 2702L, 2703L, 2704L, 2705L, 2706L,
2707L, 2708L, 2709L, 2710L, 2711L, 2712L, 2713L, 2714L, 2715L,
2716L, 2717L, 2718L, 2719L, 2720L, 2721L, 2722L, 2723L, 2724L,
2725L, 2726L, 2727L, 2728L, 2729L, 2730L, 2731L, 2732L, 2733L,
2734L, 2735L, 2736L, 2737L, 2738L, 2739L, 2740L, 2741L, 2742L,
2743L, 2744L, 2745L, 2746L, 2747L, 2748L, 2749L, 2750L, 2751L,
2752L, 2753L, 2754L, 2755L, 2756L, 2757L, 2758L, 2759L, 2760L,
2761L, 2762L, 2763L, 2764L, 2765L, 2766L, 2767L, 2768L, 2769L,
2770L, 2771L, 2772L, 2773L, 2774L, 2775L, 2776L, 2777L, 2778L,
2779L, 2780L, 2781L, 2782L, 2783L, 2784L, 2785L, 2786L, 2787L,
2788L, 2789L, 2790L, 2791L, 2792L, 2793L, 2794L, 2795L, 2796L,
2797L, 2798L, 2799L, 2800L, 2851L, 2852L, 2853L, 2854L, 2855L,
2856L, 2857L, 2858L, 2859L, 2860L, 2861L, 2862L, 2863L, 2864L,
2865L, 2866L, 2867L, 2868L, 2869L, 2870L, 2871L, 2872L, 2873L,
2874L, 2875L, 2876L, 2877L, 2878L, 2879L, 2880L, 2881L, 2882L,
2883L, 2884L, 2885L, 2886L, 2887L, 2888L, 2889L, 2890L, 2891L,
2892L, 2893L, 2894L, 2895L, 2896L, 2897L, 2898L, 2899L, 2900L,
2901L, 2902L, 2903L, 2904L, 2905L, 2906L, 2907L, 2908L, 2909L,
2910L, 2911L, 2912L, 2913L, 2914L, 2915L, 2916L, 2917L, 2918L,
2919L, 2920L, 2921L, 2922L, 2923L, 2924L, 2925L, 2926L, 2927L,
2928L, 2929L, 2930L, 2931L, 2932L, 2933L, 2934L, 2935L, 2936L,
2937L, 2938L, 2939L, 2940L, 2941L, 2942L, 2943L, 2944L, 2945L,
2946L, 2947L, 2948L, 2949L, 2950L, 3001L, 3002L, 3003L, 3004L,
3005L, 3006L, 3007L, 3008L, 3009L, 3010L, 3011L, 3012L, 3013L,
3014L, 3015L, 3016L, 3017L, 3018L, 3019L, 3020L, 3021L, 3022L,
3023L, 3024L, 3025L, 3026L, 3027L, 3028L, 3029L, 3030L, 3031L,
3032L, 3033L, 3034L, 3035L, 3036L, 3037L, 3038L, 3039L, 3040L,
3041L, 3042L, 3043L, 3044L, 3045L, 3046L, 3047L, 3048L, 3049L,
3050L, 3051L, 3052L, 3053L, 3054L, 3055L, 3056L, 3057L, 3058L,
3059L, 3060L, 3061L, 3062L, 3063L, 3064L, 3065L, 3066L, 3067L,
3068L, 3069L, 3070L, 3071L, 3072L, 3073L, 3074L, 3075L, 3076L,
3077L, 3078L, 3079L, 3080L, 3081L, 3082L, 3083L, 3084L, 3085L,
3086L, 3087L, 3088L, 3089L, 3090L, 3091L, 3092L, 3093L, 3094L,
3095L, 3096L, 3097L, 3098L, 3099L, 3100L, 3151L, 3152L, 3153L,
3154L, 3155L, 3156L, 3157L, 3158L, 3159L, 3160L, 3161L, 3162L,
3163L, 3164L, 3165L, 3166L, 3167L, 3168L, 3169L, 3170L, 3171L,
3172L, 3173L, 3174L, 3175L, 3176L, 3177L, 3178L, 3179L, 3180L,
3181L, 3182L, 3183L, 3184L, 3185L, 3186L, 3187L, 3188L, 3189L,
3190L, 3191L, 3192L, 3193L, 3194L, 3195L, 3196L, 3197L, 3198L,
3199L, 3200L, 3201L, 3202L, 3203L, 3204L, 3205L, 3206L, 3207L,
3208L, 3209L, 3210L, 3211L, 3212L, 3213L, 3214L, 3215L, 3216L,
3217L, 3218L, 3219L, 3220L, 3221L, 3222L, 3223L, 3224L, 3225L,
3226L, 3227L, 3228L, 3229L, 3230L, 3231L, 3232L, 3233L, 3234L,
3235L, 3236L, 3237L, 3238L, 3239L, 3240L, 3241L, 3242L, 3243L,
3244L, 3245L, 3246L, 3247L, 3248L, 3249L, 3250L, 3301L, 3302L,
3303L, 3304L, 3305L, 3306L, 3307L, 3308L, 3309L, 3310L, 3311L,
3312L, 3313L, 3314L, 3315L, 3316L, 3317L, 3318L, 3319L, 3320L,
3321L, 3322L, 3323L, 3324L, 3325L, 3326L, 3327L, 3328L, 3329L,
3330L, 3331L, 3332L, 3333L, 3334L, 3335L, 3336L, 3337L, 3338L,
3339L, 3340L, 3341L, 3342L, 3343L, 3344L, 3345L, 3346L, 3347L,
3348L, 3349L, 3350L, 3351L, 3352L, 3353L, 3354L, 3355L, 3356L,
3357L, 3358L, 3359L, 3360L, 3361L, 3362L, 3363L, 3364L, 3365L,
3366L, 3367L, 3368L, 3369L, 3370L, 3371L, 3372L, 3373L, 3374L,
3375L, 3376L, 3377L, 3378L, 3379L, 3380L, 3381L, 3382L, 3383L,
3384L, 3385L, 3386L, 3387L, 3388L, 3389L, 3390L, 3391L, 3392L,
3393L, 3394L, 3395L, 3396L, 3397L, 3398L, 3399L, 3400L, 3451L,
3452L, 3453L, 3454L, 3455L, 3456L, 3457L, 3458L, 3459L, 3460L,
3461L, 3462L, 3463L, 3464L, 3465L, 3466L, 3467L, 3468L, 3469L,
3470L, 3471L, 3472L, 3473L, 3474L, 3475L, 3476L, 3477L, 3478L,
3479L, 3480L, 3481L, 3482L, 3483L, 3484L, 3485L, 3486L, 3487L,
3488L, 3489L, 3490L, 3491L, 3492L, 3493L, 3494L, 3495L, 3496L,
3497L, 3498L, 3499L, 3500L, 3501L, 3502L, 3503L, 3504L, 3505L,
3506L, 3507L, 3508L, 3509L, 3510L, 3511L, 3512L, 3513L, 3514L,
3515L, 3516L, 3517L, 3518L, 3519L, 3520L, 3521L, 3522L, 3523L,
3524L, 3525L, 3526L, 3527L, 3528L, 3529L, 3530L, 3531L, 3532L,
3533L, 3534L, 3535L, 3536L, 3537L, 3538L, 3539L, 3540L, 3541L,
3542L, 3543L, 3544L, 3545L, 3546L, 3547L, 3548L, 3549L, 3550L,
3601L, 3602L, 3603L, 3604L, 3605L, 3606L, 3607L, 3608L, 3609L,
3610L, 3611L, 3612L, 3613L, 3614L, 3615L, 3616L, 3617L, 3618L,
3619L, 3620L, 3621L, 3622L, 3623L, 3624L, 3625L, 3626L, 3627L,
3628L, 3629L, 3630L, 3631L, 3632L, 3633L, 3634L, 3635L, 3636L,
3637L, 3638L, 3639L, 3640L, 3641L, 3642L, 3643L, 3644L, 3645L,
3646L, 3647L, 3648L, 3649L, 3650L, 3651L, 3652L, 3653L, 3654L,
3655L, 3656L, 3657L, 3658L, 3659L, 3660L, 3661L, 3662L, 3663L,
3664L, 3665L, 3666L, 3667L, 3668L, 3669L, 3670L, 3671L, 3672L,
3673L, 3674L, 3675L, 3676L, 3677L, 3678L, 3679L, 3680L, 3681L,
3682L, 3683L, 3684L, 3685L, 3686L, 3687L, 3688L, 3689L, 3690L,
3691L, 3692L, 3693L, 3694L, 3695L, 3696L, 3697L, 3698L, 3699L,
3700L))
## ----ComBox1, echo=FALSE, result="hide", fig.cap="Boxplots for tail intensities in Comet assay.", message=FALSE, warning=FALSE, fig.pos="H", fig.height=4.5, fig.width=6.5, fig.align='center', dev="png",dpi=300----
boxplot(Tail.intensity ~ Treatment, data=TIComet, ylab="Tail Intensity", xlab="Dose")
points(y=TIComet$Tail.intensity, x=TIComet$Treatment, col=as.integer(TIComet$Animal_no))
## ----techn, echo=TRUE,results='asis', warning=FALSE, message=FALSE------------
TIComet$Dose[TIComet$Treatment=="VC"] <-0
TIComet$Dose[TIComet$Treatment=="Low"] <-25
TIComet$Dose[TIComet$Treatment=="medium"] <-75
TIComet$Dose[TIComet$Treatment=="High"] <-150
TIComet$Dose[TIComet$Treatment=="Superhigh"] <-500
TIComet$logTI <-log(TIComet$Tail.intensity+0.001)
mixL <- lmer(logTI ~ Dose+(1|Animal_no/Sample), data=TIComet)
EXB <- tukeytrendfit(mixL, dose="Dose", scaling=c("ari", "ord", "arilog"))
EXBB <- summary(glht(model=EXB$mmm, linfct=EXB$mlf))
## ----FENU, echo=FALSE, results='hide', warning=FALSE, message=FALSE-----------
fenu <-
structure(list(subject = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L,
4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L,
9L, 9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 13L,
13L, 14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L,
18L, 18L, 18L), period = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), trt = c(4L,
8L, 0L, 4L, 0L, 8L, 8L, 4L, 0L, 0L, 4L, 8L, 8L, 0L, 4L, 0L, 8L,
4L, 0L, 4L, 8L, 0L, 8L, 4L, 8L, 4L, 0L, 4L, 0L, 8L, 4L, 8L, 0L,
8L, 0L, 4L, 8L, 4L, 0L, 4L, 8L, 0L, 8L, 0L, 4L, 0L, 4L, 8L, 4L,
0L, 8L, 0L, 8L, 4L), Ad_Lib_Lunch = c(1189.756, 576.962, 641.998,
1554.898, 1297.948, 1245.796, 1261.746, 1169.432, 1126.988, 1078.072,
1071.87, 825.067, 620.38, 1134.193, 1394.231, 727.288, 728.063,
986.057, 957.849, 1136.109, 957.413, 2156.761, 1656.231, 1769.438,
1206.818, 1204.818, 1385.801, 1011.428, 1345.279, 1173.188, 1173.106,
1066.703, 1137.53, 1745.437, 1136.524, 1630.337, 1035.507, 1568.084,
1786.647, 1327.06, 1219.183, 1276.848, 2068.996, 2616.345, 2161.583,
1363.187, 1379.031, 1676.081, 1398.99, 794.473, 526.105, 1157.478,
1240.915, 1207.228)), .Names = c("subject", "period", "trt",
"Ad_Lib_Lunch"), class = "data.frame", row.names = c(NA, -54L
))
## ----fenu2, echo=TRUE, warning=FALSE, message=FALSE---------------------------
library("lme4")
modFE <- lmer(Ad_Lib_Lunch ~ trt + period + (1| subject), REML = TRUE, data=fenu)
ttfe <- tukeytrendfit(modFE, dose="trt", scaling=c("ari", "ord", "arilog"), ddf="KR")
compfe <- summary(glht(model=ttfe$mmm, linfct=ttfe$mlf))
compfe
## ----fenucorr, echo=TRUE, warning=FALSE, message=FALSE------------------------
round(cov2cor(vcov(compfe)), digits=2)
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