Nothing
afe-Ex.R
In afe: Analysis of Factorial Experiments
pkgname <- "afe"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
options(pager = "console")
library('afe')
assign(".oldSearch", search(), pos = 'CheckExEnv')
cleanEx()
nameEx("aov.car")
### * aov.car
flush(stderr()); flush(stdout())
### Name: aov.car
### Title: Convenience wrappers for car::Anova using either a formula or
### factor based interface.
### Aliases: aov.car ez.glm univariate
### ** Examples
# exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
data(obk.long)
# run univariate mixed ANCOVA for the full design:
univariate(aov.car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long))
univariate(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
# both calls return the same:
## $anova
## SS num Df Error SS den Df F Pr(>F)
## (Intercept) 6454.236987 1 215.65658 9 269.3547893 5.152317e-08
## treatment 171.399953 2 215.65658 9 3.5765187 7.193619e-02
## gender 94.598340 1 215.65658 9 3.9478742 7.818280e-02
## age 12.398975 1 215.65658 9 0.5174466 4.901885e-01
## treatment:gender 61.531858 2 215.65658 9 1.2839551 3.231798e-01
## phase 134.586005 2 59.72439 18 20.2810632 2.448505e-05
## treatment:phase 80.604542 4 59.72439 18 6.0732385 2.826803e-03
## gender:phase 1.634246 2 59.72439 18 0.2462681 7.843036e-01
## age:phase 20.553392 2 59.72439 18 3.0972362 6.982439e-02
## treatment:gender:phase 21.254421 4 59.72439 18 1.6014379 2.170946e-01
## hour 108.513510 4 47.59543 36 20.5192290 7.001584e-09
## treatment:hour 7.547869 8 47.59543 36 0.7136275 6.779072e-01
## gender:hour 3.746135 4 47.59543 36 0.7083708 5.915285e-01
## age:hour 14.904567 4 47.59543 36 2.8183608 3.926421e-02
## treatment:gender:hour 6.235198 8 47.59543 36 0.5895186 7.798264e-01
## phase:hour 9.762579 8 88.62706 72 0.9913814 4.501348e-01
## treatment:phase:hour 6.579092 16 88.62706 72 0.3340505 9.915014e-01
## gender:phase:hour 8.851396 8 88.62706 72 0.8988515 5.222336e-01
## age:phase:hour 7.539611 8 88.62706 72 0.7656409 6.339004e-01
## treatment:gender:phase:hour 12.822199 16 88.62706 72 0.6510416 8.307936e-01
##
## $mauchly
## Test statistic p-value
## phase 0.8217571566 0.45600959
## treatment:phase 0.8217571566 0.45600959
## gender:phase 0.8217571566 0.45600959
## age:phase 0.8217571566 0.45600959
## treatment:gender:phase 0.8217571566 0.45600959
## hour 0.0966749877 0.04923980
## treatment:hour 0.0966749877 0.04923980
## gender:hour 0.0966749877 0.04923980
## age:hour 0.0966749877 0.04923980
## treatment:gender:hour 0.0966749877 0.04923980
## phase:hour 0.0002379741 0.08651564
## treatment:phase:hour 0.0002379741 0.08651564
## gender:phase:hour 0.0002379741 0.08651564
## age:phase:hour 0.0002379741 0.08651564
## treatment:gender:phase:hour 0.0002379741 0.08651564
##
## $sphericity.correction
## GG eps Pr(>F[GG]) HF eps Pr(>F[HF])
## phase 0.8487215 8.383485e-05 1.0252867 2.448505e-05
## treatment:phase 0.8487215 5.159591e-03 1.0252867 2.826803e-03
## gender:phase 0.8487215 7.493990e-01 1.0252867 7.843036e-01
## age:phase 0.8487215 8.073373e-02 1.0252867 6.982439e-02
## treatment:gender:phase 0.8487215 2.279698e-01 1.0252867 2.170946e-01
## hour 0.5341747 1.302016e-05 0.7054545 8.046331e-07
## treatment:hour 0.5341747 6.010781e-01 0.7054545 6.342676e-01
## gender:hour 0.5341747 5.137213e-01 0.7054545 5.478398e-01
## age:hour 0.5341747 8.155027e-02 0.7054545 6.211130e-02
## treatment:gender:hour 0.5341747 6.843526e-01 0.7054545 7.263729e-01
## phase:hour 0.4355822 4.186799e-01 0.7444364 4.402119e-01
## treatment:phase:hour 0.4355822 9.317848e-01 0.7444364 9.787985e-01
## gender:phase:hour 0.4355822 4.651930e-01 0.7444364 5.020890e-01
## age:phase:hour 0.4355822 5.395151e-01 0.7444364 5.992844e-01
## treatment:gender:phase:hour 0.4355822 7.100921e-01 0.7444364 7.878433e-01
##
## Warning message:
## In univariate(aov.car(value ~ treatment * gender + age + Error(id/phase * :
## HF eps > 1 treated as 1
# To get a nicer ANOVA table use function nice.anova (see ?noce.anova):
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
## Effect df MSE F p
## 1 treatment 2, 9 23.96 3.58 + .07
## 2 gender 1, 9 23.96 3.95 + .08
## 3 age 1, 9 23.96 0.52 .49
## 4 treatment:gender 2, 9 23.96 1.28 .32
## 5 phase 1.7, 15.28 3.91 20.28 *** <.001
## 6 treatment:phase 3.39, 15.28 3.91 6.07 ** .005
## 7 gender:phase 1.7, 15.28 3.91 0.25 .75
## 8 age:phase 1.7, 15.28 3.91 3.10 + .08
## 9 treatment:gender:phase 3.39, 15.28 3.91 1.60 .23
## 10 hour 2.14, 19.23 2.48 20.52 *** <.001
## 11 treatment:hour 4.27, 19.23 2.48 0.71 .60
## 12 gender:hour 2.14, 19.23 2.48 0.71 .51
## 13 age:hour 2.14, 19.23 2.48 2.82 + .08
## 14 treatment:gender:hour 4.27, 19.23 2.48 0.59 .68
## 15 phase:hour 3.48, 31.36 2.83 0.99 .42
## 16 treatment:phase:hour 6.97, 31.36 2.83 0.33 .93
## 17 gender:phase:hour 3.48, 31.36 2.83 0.90 .47
## 18 age:phase:hour 3.48, 31.36 2.83 0.77 .54
## 19 treatment:gender:phase:hour 6.97, 31.36 2.83 0.65 .71
# replicating ?Anova using aov.car:
aov.car(value ~ treatment * gender + Error(id/phase*hour), data = obk.long, type = 2)
# in contrast to aov you do not need the within-subject factors outside Error()
# replicating ?Anova using ez.glm:
ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), type = 2)
#both return:
## Type II Repeated Measures MANOVA Tests: Pillai test statistic
## Df test stat approx F num Df den Df Pr(>F)
## (Intercept) 1 0.970 318 1 10 0.0000000065 ***
## treatment 2 0.481 5 2 10 0.03769 *
## gender 1 0.204 3 1 10 0.14097
## treatment:gender 2 0.364 3 2 10 0.10447
## phase 1 0.851 26 2 9 0.00019 ***
## treatment:phase 2 0.685 3 4 20 0.06674 .
## gender:phase 1 0.043 0 2 9 0.82000
## treatment:gender:phase 2 0.311 1 4 20 0.47215
## hour 1 0.935 25 4 7 0.00030 ***
## treatment:hour 2 0.301 0 8 16 0.92952
## gender:hour 1 0.293 1 4 7 0.60237
## treatment:gender:hour 2 0.570 1 8 16 0.61319
## phase:hour 1 0.550 0 8 3 0.83245
## treatment:phase:hour 2 0.664 0 16 8 0.99144
## gender:phase:hour 1 0.695 1 8 3 0.62021
## treatment:gender:phase:hour 2 0.793 0 16 8 0.97237
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# aggregating over one within-subjects factor (phase) with warning:
aov.car(value ~ treatment * gender + age + Error(id/hour), data = obk.long)
ez.glm("id", "value", obk.long, c("treatment", "gender"), "hour", "age")
# runs with "numeric" factors
obk.long$hour2 <- as.numeric(as.character(obk.long$hour))
aov.car(value ~ treatment * gender + Error(id/hour2), data = obk.long, type = 2)
# only between
aov.car(value ~ treatment * gender + age + Error(id), data = obk.long, type = 2)
aov.car(value ~ treatment * gender + Error(id), data = obk.long, type = 2)
ez.glm("id", "value", obk.long, c("treatment", "gender"), within = NULL, covariate = "age", type = 2, print.formula = TRUE)
ez.glm("id", "value", obk.long, c("treatment", "gender"), within = NULL, type = 2, print.formula = TRUE)
# only within
univariate(aov.car(value ~ Error(id/phase*hour), data = obk.long, type = 2))
univariate(ez.glm("id", "value", obk.long, NULL, c("phase", "hour"), type = 2, print.formula = TRUE))
cleanEx()
nameEx("mixed")
### * mixed
flush(stderr()); flush(stdout())
### Name: mixed
### Title: Obtain p-values for a mixed-model from lmer().
### Aliases: mixed
### ** Examples
## Not run:
##D # example data from package languageR:
##D # Lexical decision latencies elicited from 21 subjects for 79 English concrete nouns, with variables linked to subject or word.
##D data(lexdec, package = "languageR")
##D
##D # using the simplest model
##D m1 <- mixed("Correct + Trial + PrevType * meanWeight + Frequency + NativeLanguage * Length", "(1|Subject) + (1|Word)", "RT", data = lexdec)
##D
##D anova(m1)
##D # gives:
##D ## Effect df1 df2 Fstat p.value
##D ## 1 (Intercept) 1 96.64 13573.0985 0.000
##D ## 2 Correct 1 1627.73 8.1452 0.004
##D ## 3 Trial 1 1592.43 7.5738 0.006
##D ## 4 PrevType 1 1605.39 0.1700 0.680
##D ## 5 meanWeight 1 75.39 14.8545 0.000
##D ## 6 Frequency 1 76.08 56.5348 0.000
##D ## 7 NativeLanguage 1 27.12 0.6953 0.412
##D ## 8 Length 1 75.83 8.6959 0.004
##D ## 9 PrevType:meanWeight 1 1601.18 6.1823 0.013
##D ## 10 NativeLanguage:Length 1 1555.49 14.2445 0.000
## End(Not run)
cleanEx()
nameEx("nice.anova")
### * nice.anova
flush(stderr()); flush(stdout())
### Name: nice.anova
### Title: Make nice ANOVA table for printing.
### Aliases: nice.anova
### ** Examples
# exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
data(obk.long)
# run univariate mixed ANCOVA for the full design:
nice.anova(aov.car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long))
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
# no between
nice.anova(ez.glm("id", "value", obk.long, NULL, c("phase", "hour")))
# no within
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender")))
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender")), sig.symbol = rep("", 4))
## Not run:
##D # use package ascii or xtable for formatting of tables ready for printing.
##D
##D full <- nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
##D
##D require(ascii)
##D print(ascii(full, include.rownames = FALSE, caption = "ANOVA 1"), type = "org")
##D
##D require(xtable)
##D print.xtable(xtable(full, caption = "ANOVA 2"), include.rownames = FALSE)
## End(Not run)
cleanEx()
nameEx("obk.long")
### * obk.long
flush(stderr()); flush(stdout())
### Name: obk.long
### Title: O'Brien Kaiser's Repeated-Measures Dataset with Covariate
### Aliases: obk.long
### Keywords: dataset datasets
### ** Examples
# The dataset is constructed as follows:
set.seed(1)
OBrienKaiser2 <- within(OBrienKaiser, {
id <- factor(1:nrow(OBrienKaiser))
age <- scale(sample(18:35, nrow(OBrienKaiser), replace = TRUE), scale = FALSE)})
attributes(OBrienKaiser2$age) <- NULL # needed or resahpe2::melt throws an error.
OBrienKaiser2$age <- as.numeric(OBrienKaiser2$age)
obk.long <- melt(OBrienKaiser2, id.vars = c("id", "treatment", "gender", "age"))
obk.long[,c("phase", "hour")] <- lapply(as.data.frame(do.call(rbind,strsplit(as.character(obk.long$variable), "\\."),)), factor)
obk.long <- obk.long[,c("id", "treatment", "gender", "age", "phase", "hour", "value")]
obk.long <- obk.long[order(obk.long$id),]
rownames(obk.long) <- NULL
str(obk.long)
## 'data.frame': 240 obs. of 7 variables:
## $ id : Factor w/ 16 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ treatment: Factor w/ 3 levels "control","A",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ age : num -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 ...
## $ phase : Factor w/ 3 levels "fup","post","pre": 3 3 3 3 3 2 2 2 2 2 ...
## $ hour : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## $ value : num 1 2 4 2 1 3 2 5 3 2 ...
head(obk.long)
## id treatment gender age phase hour value
## 1 1 control M -4.75 pre 1 1
## 2 1 control M -4.75 pre 2 2
## 3 1 control M -4.75 pre 3 4
## 4 1 control M -4.75 pre 4 2
## 5 1 control M -4.75 pre 5 1
## 6 1 control M -4.75 post 1 3
### * <FOOTER>
###
cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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pkgname <- "afe"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
options(pager = "console")
library('afe')
assign(".oldSearch", search(), pos = 'CheckExEnv')
cleanEx()
nameEx("aov.car")
### * aov.car
flush(stderr()); flush(stdout())
### Name: aov.car
### Title: Convenience wrappers for car::Anova using either a formula or
### factor based interface.
### Aliases: aov.car ez.glm univariate
### ** Examples
# exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
data(obk.long)
# run univariate mixed ANCOVA for the full design:
univariate(aov.car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long))
univariate(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
# both calls return the same:
## $anova
## SS num Df Error SS den Df F Pr(>F)
## (Intercept) 6454.236987 1 215.65658 9 269.3547893 5.152317e-08
## treatment 171.399953 2 215.65658 9 3.5765187 7.193619e-02
## gender 94.598340 1 215.65658 9 3.9478742 7.818280e-02
## age 12.398975 1 215.65658 9 0.5174466 4.901885e-01
## treatment:gender 61.531858 2 215.65658 9 1.2839551 3.231798e-01
## phase 134.586005 2 59.72439 18 20.2810632 2.448505e-05
## treatment:phase 80.604542 4 59.72439 18 6.0732385 2.826803e-03
## gender:phase 1.634246 2 59.72439 18 0.2462681 7.843036e-01
## age:phase 20.553392 2 59.72439 18 3.0972362 6.982439e-02
## treatment:gender:phase 21.254421 4 59.72439 18 1.6014379 2.170946e-01
## hour 108.513510 4 47.59543 36 20.5192290 7.001584e-09
## treatment:hour 7.547869 8 47.59543 36 0.7136275 6.779072e-01
## gender:hour 3.746135 4 47.59543 36 0.7083708 5.915285e-01
## age:hour 14.904567 4 47.59543 36 2.8183608 3.926421e-02
## treatment:gender:hour 6.235198 8 47.59543 36 0.5895186 7.798264e-01
## phase:hour 9.762579 8 88.62706 72 0.9913814 4.501348e-01
## treatment:phase:hour 6.579092 16 88.62706 72 0.3340505 9.915014e-01
## gender:phase:hour 8.851396 8 88.62706 72 0.8988515 5.222336e-01
## age:phase:hour 7.539611 8 88.62706 72 0.7656409 6.339004e-01
## treatment:gender:phase:hour 12.822199 16 88.62706 72 0.6510416 8.307936e-01
##
## $mauchly
## Test statistic p-value
## phase 0.8217571566 0.45600959
## treatment:phase 0.8217571566 0.45600959
## gender:phase 0.8217571566 0.45600959
## age:phase 0.8217571566 0.45600959
## treatment:gender:phase 0.8217571566 0.45600959
## hour 0.0966749877 0.04923980
## treatment:hour 0.0966749877 0.04923980
## gender:hour 0.0966749877 0.04923980
## age:hour 0.0966749877 0.04923980
## treatment:gender:hour 0.0966749877 0.04923980
## phase:hour 0.0002379741 0.08651564
## treatment:phase:hour 0.0002379741 0.08651564
## gender:phase:hour 0.0002379741 0.08651564
## age:phase:hour 0.0002379741 0.08651564
## treatment:gender:phase:hour 0.0002379741 0.08651564
##
## $sphericity.correction
## GG eps Pr(>F[GG]) HF eps Pr(>F[HF])
## phase 0.8487215 8.383485e-05 1.0252867 2.448505e-05
## treatment:phase 0.8487215 5.159591e-03 1.0252867 2.826803e-03
## gender:phase 0.8487215 7.493990e-01 1.0252867 7.843036e-01
## age:phase 0.8487215 8.073373e-02 1.0252867 6.982439e-02
## treatment:gender:phase 0.8487215 2.279698e-01 1.0252867 2.170946e-01
## hour 0.5341747 1.302016e-05 0.7054545 8.046331e-07
## treatment:hour 0.5341747 6.010781e-01 0.7054545 6.342676e-01
## gender:hour 0.5341747 5.137213e-01 0.7054545 5.478398e-01
## age:hour 0.5341747 8.155027e-02 0.7054545 6.211130e-02
## treatment:gender:hour 0.5341747 6.843526e-01 0.7054545 7.263729e-01
## phase:hour 0.4355822 4.186799e-01 0.7444364 4.402119e-01
## treatment:phase:hour 0.4355822 9.317848e-01 0.7444364 9.787985e-01
## gender:phase:hour 0.4355822 4.651930e-01 0.7444364 5.020890e-01
## age:phase:hour 0.4355822 5.395151e-01 0.7444364 5.992844e-01
## treatment:gender:phase:hour 0.4355822 7.100921e-01 0.7444364 7.878433e-01
##
## Warning message:
## In univariate(aov.car(value ~ treatment * gender + age + Error(id/phase * :
## HF eps > 1 treated as 1
# To get a nicer ANOVA table use function nice.anova (see ?noce.anova):
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
## Effect df MSE F p
## 1 treatment 2, 9 23.96 3.58 + .07
## 2 gender 1, 9 23.96 3.95 + .08
## 3 age 1, 9 23.96 0.52 .49
## 4 treatment:gender 2, 9 23.96 1.28 .32
## 5 phase 1.7, 15.28 3.91 20.28 *** <.001
## 6 treatment:phase 3.39, 15.28 3.91 6.07 ** .005
## 7 gender:phase 1.7, 15.28 3.91 0.25 .75
## 8 age:phase 1.7, 15.28 3.91 3.10 + .08
## 9 treatment:gender:phase 3.39, 15.28 3.91 1.60 .23
## 10 hour 2.14, 19.23 2.48 20.52 *** <.001
## 11 treatment:hour 4.27, 19.23 2.48 0.71 .60
## 12 gender:hour 2.14, 19.23 2.48 0.71 .51
## 13 age:hour 2.14, 19.23 2.48 2.82 + .08
## 14 treatment:gender:hour 4.27, 19.23 2.48 0.59 .68
## 15 phase:hour 3.48, 31.36 2.83 0.99 .42
## 16 treatment:phase:hour 6.97, 31.36 2.83 0.33 .93
## 17 gender:phase:hour 3.48, 31.36 2.83 0.90 .47
## 18 age:phase:hour 3.48, 31.36 2.83 0.77 .54
## 19 treatment:gender:phase:hour 6.97, 31.36 2.83 0.65 .71
# replicating ?Anova using aov.car:
aov.car(value ~ treatment * gender + Error(id/phase*hour), data = obk.long, type = 2)
# in contrast to aov you do not need the within-subject factors outside Error()
# replicating ?Anova using ez.glm:
ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), type = 2)
#both return:
## Type II Repeated Measures MANOVA Tests: Pillai test statistic
## Df test stat approx F num Df den Df Pr(>F)
## (Intercept) 1 0.970 318 1 10 0.0000000065 ***
## treatment 2 0.481 5 2 10 0.03769 *
## gender 1 0.204 3 1 10 0.14097
## treatment:gender 2 0.364 3 2 10 0.10447
## phase 1 0.851 26 2 9 0.00019 ***
## treatment:phase 2 0.685 3 4 20 0.06674 .
## gender:phase 1 0.043 0 2 9 0.82000
## treatment:gender:phase 2 0.311 1 4 20 0.47215
## hour 1 0.935 25 4 7 0.00030 ***
## treatment:hour 2 0.301 0 8 16 0.92952
## gender:hour 1 0.293 1 4 7 0.60237
## treatment:gender:hour 2 0.570 1 8 16 0.61319
## phase:hour 1 0.550 0 8 3 0.83245
## treatment:phase:hour 2 0.664 0 16 8 0.99144
## gender:phase:hour 1 0.695 1 8 3 0.62021
## treatment:gender:phase:hour 2 0.793 0 16 8 0.97237
## ---
## Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# aggregating over one within-subjects factor (phase) with warning:
aov.car(value ~ treatment * gender + age + Error(id/hour), data = obk.long)
ez.glm("id", "value", obk.long, c("treatment", "gender"), "hour", "age")
# runs with "numeric" factors
obk.long$hour2 <- as.numeric(as.character(obk.long$hour))
aov.car(value ~ treatment * gender + Error(id/hour2), data = obk.long, type = 2)
# only between
aov.car(value ~ treatment * gender + age + Error(id), data = obk.long, type = 2)
aov.car(value ~ treatment * gender + Error(id), data = obk.long, type = 2)
ez.glm("id", "value", obk.long, c("treatment", "gender"), within = NULL, covariate = "age", type = 2, print.formula = TRUE)
ez.glm("id", "value", obk.long, c("treatment", "gender"), within = NULL, type = 2, print.formula = TRUE)
# only within
univariate(aov.car(value ~ Error(id/phase*hour), data = obk.long, type = 2))
univariate(ez.glm("id", "value", obk.long, NULL, c("phase", "hour"), type = 2, print.formula = TRUE))
cleanEx()
nameEx("mixed")
### * mixed
flush(stderr()); flush(stdout())
### Name: mixed
### Title: Obtain p-values for a mixed-model from lmer().
### Aliases: mixed
### ** Examples
## Not run:
##D # example data from package languageR:
##D # Lexical decision latencies elicited from 21 subjects for 79 English concrete nouns, with variables linked to subject or word.
##D data(lexdec, package = "languageR")
##D
##D # using the simplest model
##D m1 <- mixed("Correct + Trial + PrevType * meanWeight + Frequency + NativeLanguage * Length", "(1|Subject) + (1|Word)", "RT", data = lexdec)
##D
##D anova(m1)
##D # gives:
##D ## Effect df1 df2 Fstat p.value
##D ## 1 (Intercept) 1 96.64 13573.0985 0.000
##D ## 2 Correct 1 1627.73 8.1452 0.004
##D ## 3 Trial 1 1592.43 7.5738 0.006
##D ## 4 PrevType 1 1605.39 0.1700 0.680
##D ## 5 meanWeight 1 75.39 14.8545 0.000
##D ## 6 Frequency 1 76.08 56.5348 0.000
##D ## 7 NativeLanguage 1 27.12 0.6953 0.412
##D ## 8 Length 1 75.83 8.6959 0.004
##D ## 9 PrevType:meanWeight 1 1601.18 6.1823 0.013
##D ## 10 NativeLanguage:Length 1 1555.49 14.2445 0.000
## End(Not run)
cleanEx()
nameEx("nice.anova")
### * nice.anova
flush(stderr()); flush(stdout())
### Name: nice.anova
### Title: Make nice ANOVA table for printing.
### Aliases: nice.anova
### ** Examples
# exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
data(obk.long)
# run univariate mixed ANCOVA for the full design:
nice.anova(aov.car(value ~ treatment * gender + age + Error(id/phase*hour), data = obk.long))
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
# no between
nice.anova(ez.glm("id", "value", obk.long, NULL, c("phase", "hour")))
# no within
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender")))
nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender")), sig.symbol = rep("", 4))
## Not run:
##D # use package ascii or xtable for formatting of tables ready for printing.
##D
##D full <- nice.anova(ez.glm("id", "value", obk.long, c("treatment", "gender"), c("phase", "hour"), "age"))
##D
##D require(ascii)
##D print(ascii(full, include.rownames = FALSE, caption = "ANOVA 1"), type = "org")
##D
##D require(xtable)
##D print.xtable(xtable(full, caption = "ANOVA 2"), include.rownames = FALSE)
## End(Not run)
cleanEx()
nameEx("obk.long")
### * obk.long
flush(stderr()); flush(stdout())
### Name: obk.long
### Title: O'Brien Kaiser's Repeated-Measures Dataset with Covariate
### Aliases: obk.long
### Keywords: dataset datasets
### ** Examples
# The dataset is constructed as follows:
set.seed(1)
OBrienKaiser2 <- within(OBrienKaiser, {
id <- factor(1:nrow(OBrienKaiser))
age <- scale(sample(18:35, nrow(OBrienKaiser), replace = TRUE), scale = FALSE)})
attributes(OBrienKaiser2$age) <- NULL # needed or resahpe2::melt throws an error.
OBrienKaiser2$age <- as.numeric(OBrienKaiser2$age)
obk.long <- melt(OBrienKaiser2, id.vars = c("id", "treatment", "gender", "age"))
obk.long[,c("phase", "hour")] <- lapply(as.data.frame(do.call(rbind,strsplit(as.character(obk.long$variable), "\\."),)), factor)
obk.long <- obk.long[,c("id", "treatment", "gender", "age", "phase", "hour", "value")]
obk.long <- obk.long[order(obk.long$id),]
rownames(obk.long) <- NULL
str(obk.long)
## 'data.frame': 240 obs. of 7 variables:
## $ id : Factor w/ 16 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ treatment: Factor w/ 3 levels "control","A",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
## $ age : num -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 -4.75 ...
## $ phase : Factor w/ 3 levels "fup","post","pre": 3 3 3 3 3 2 2 2 2 2 ...
## $ hour : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## $ value : num 1 2 4 2 1 3 2 5 3 2 ...
head(obk.long)
## id treatment gender age phase hour value
## 1 1 control M -4.75 pre 1 1
## 2 1 control M -4.75 pre 2 2
## 3 1 control M -4.75 pre 3 4
## 4 1 control M -4.75 pre 4 2
## 5 1 control M -4.75 pre 5 1
## 6 1 control M -4.75 post 1 3
### * <FOOTER>
###
cat("Time elapsed: ", proc.time() - get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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