Description Author(s) References See Also Examples
Exam3.3 use RCBD data with fixed location effect and different forms of estimable functions are shown in this example.
Muhammad Yaseen (myaseen208@gmail.com)
Adeela Munawar (adeela.uaf@gmail.com)
Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
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## linear model for Gaussian data
#-----------------------------------------------------------------------------------
data(DataSet3.2)
DataSet3.2$trt <- factor(x = DataSet3.2$trt, level = c(3,0,1,2))
DataSet3.2$loc <- factor(x = DataSet3.2$loc, level = c(8, 1, 2, 3, 4, 5, 6, 7))
Exam3.3.lm1 <-
lm(
formula = Y~ trt+loc
, data = DataSet3.2
# , subset
# , weights
# , na.action
, method = "qr"
, model = TRUE
# , x = FALSE
# , y = FALSE
, qr = TRUE
, singular.ok = TRUE
, contrasts = NULL
# , offset
# , ...
)
summary( Exam3.3.lm1 )
#-------------------------------------------------------------
## Individula least squares treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.3 <-
lsmeans::lsmeans(
object = Exam3.3.lm1
, specs = "trt"
# , ...
)
)
#---------------------------------------------------
## Pairwise treatment means estimate
#---------------------------------------------------
contrast( object = Lsm3.3 , method = "pairwise")
#---------------------------------------------------
## Repairwise treatment means estimate
#---------------------------------------------------
## contrast( object = Lsm3.3 , method = "repairwise")
#-------------------------------------------------------
## LSM Trt0 (This term is used in Walter Stroups' book)
#-------------------------------------------------------
library(phia)
list3.3.1 <- list(trt=c("0" = 1 ) )
Test3.3.1 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.1)
)
#-------------------------------------------------------
## LSM Trt0 alt(This term is used in Walter Stroups' book)
#-------------------------------------------------------
list3.3.2 <-
list(trt=c("0" = 1 )
, loc=c("1" = 0,"2" = 0,"3" = 0,"4" = 0,"5" = 0,"6" = 0,"7" = 0)
)
Test3.3.2 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.2)
)
#-------------------------------------------------------
## Trt0 Vs Trt1
#-------------------------------------------------------
list3.3.3 <- list(trt=c("0" = 1,"1" = -1))
Test3.3.3 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.3)
)
#-------------------------------------------------------
## average Trt0+1
#-------------------------------------------------------
list3.3.4 <- list(trt=c("0" = 0.5 , "1" = 0.5))
Test3.3.4 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.4)
)
#-------------------------------------------------------
## average Trt0+2+3
#-------------------------------------------------------
list3.3.5 <- list(trt=c("0" = 0.33333,"2" = 0.33333,"3" = 0.33333))
Test3.3.5 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.5)
)
#-------------------------------------------------------
## Trt 2 Vs 3 difference
#-------------------------------------------------------
list3.3.6 <- list(trt=c("2" = 1,"3" = -1))
Test3.3.6 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.6)
)
#-------------------------------------------------------
## Trt 1 Vs 2 difference
#-------------------------------------------------------
list3.3.7 <- list(trt=c("1" = 1,"2" = -1))
Test3.3.7 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.7)
)
#-------------------------------------------------------
## Trt 1 Vs 3 difference
#-------------------------------------------------------
list3.3.8 <- list(trt=c("1" = 1,"3" = -1))
Test3.3.8 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.8)
)
#-------------------------------------------------------
## Average trt0+1 vs Average Trt2+3
#-------------------------------------------------------
list3.3.9 <- list(trt=c("0" = 0.5,"1" = 0.5,"2" = -0.5,"3" = -0.5))
Test3.3.9 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.9)
)
#-------------------------------------------------------
## Trt1 vs Average Trt0+1+2
#-------------------------------------------------------
list3.3.10 <- list(trt=c("0" = 0.33333,"1" = -1,"2" = 0.33333,"3" = 0.33333))
Test3.3.10 <-
summary(testFactors(
model = Exam3.3.lm1
, levels = list3.3.10)
)
#-------------------------------------------------------
## Sidak Multiplicity adjustment for p-values
#-------------------------------------------------------
library(mutoss)
PValues3.3 <-
c(
Test3.3.3[[7]][1, 4]
, Test3.3.6[[7]][1, 4]
, Test3.3.7[[7]][1, 4]
, Test3.3.8[[7]][1, 4]
, Test3.3.9[[7]][1, 4]
, Test3.3.10[[7]][1, 4]
)
AdjPValues3.3 <- sidak(PValues3.3)
|
Call:
lm(formula = Y ~ trt + loc, data = DataSet3.2, method = "qr",
model = TRUE, qr = TRUE, singular.ok = TRUE, contrasts = NULL)
Residuals:
Min 1Q Median 3Q Max
-2.7750 -0.7875 0.3625 1.0813 2.3000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.2500 0.9945 28.407 < 2e-16 ***
trt0 -2.1375 0.8481 -2.520 0.01988 *
trt1 -2.9500 0.8481 -3.478 0.00224 **
trt2 0.2875 0.8481 0.339 0.73798
loc1 -0.9750 1.1994 -0.813 0.42538
loc2 -3.2500 1.1994 -2.710 0.01312 *
loc3 -1.6750 1.1994 -1.397 0.17713
loc4 -0.3500 1.1994 -0.292 0.77329
loc5 0.8250 1.1994 0.688 0.49907
loc6 -0.3000 1.1994 -0.250 0.80492
loc7 -3.5750 1.1994 -2.981 0.00713 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.696 on 21 degrees of freedom
Multiple R-squared: 0.6818, Adjusted R-squared: 0.5303
F-statistic: 4.5 on 10 and 21 DF, p-value: 0.001795
Loading required package: estimability
trt lsmean SE df lower.CL upper.CL
3 27.0875 0.5996899 21 25.84038 28.33462
0 24.9500 0.5996899 21 23.70288 26.19712
1 24.1375 0.5996899 21 22.89038 25.38462
2 27.3750 0.5996899 21 26.12788 28.62212
Results are averaged over the levels of: loc
Confidence level used: 0.95
contrast estimate SE df t.ratio p.value
3 - 0 2.1375 0.8480896 21 2.520 0.0856
3 - 1 2.9500 0.8480896 21 3.478 0.0111
3 - 2 -0.2875 0.8480896 21 -0.339 0.9862
0 - 1 0.8125 0.8480896 21 0.958 0.7742
0 - 2 -2.4250 0.8480896 21 -2.859 0.0430
1 - 2 -3.2375 0.8480896 21 -3.817 0.0051
Results are averaged over the levels of: loc
P value adjustment: tukey method for comparing a family of 4 estimates
Loading required package: car
Loading required package: mvtnorm
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