Description Author(s) References See Also Examples
Examp2.5.4.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.
Muhammad Yaseen (myaseen208@gmail.com)
Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
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## Example 2.5.4.1 p-74
#-------------------------------------------------------------
# PROC MIXED DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=drug dose drug*dose / ddfm=satterth;
# RANDOM region drug*region;
# ESTIMATE 'Samorin mean' INTERCEPT 1 drug 0 1 dose 0.5 0.5
# drug*dose 0 0 0.5 0.5;
# RUN;
# PROC GLM DATA=ex125;
# CLASS drug dose region;
# MODEL pcv=region drug region*drug dose drug*dose;
# ESTIMATE 'Samorin mean' INTERCEPT 1 drug 0 1 dose 0.5 0.5
# drug*dose 0 0 0.5 0.5;
# RUN;
library(lme4)
str(ex125)
ex125$Region1 <- factor(ex125$Region)
fm2.13 <-
lmerTest::lmer(
formula = Pcv ~ dose*Drug + (1|Region/Drug)
, data = ex125
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm2.13)
library(multcomp)
Contrasts2 <-
matrix(c(
1, 0.5, 0, 0
)
, ncol = 4
, byrow = TRUE
, dimnames = list(
c("C5")
, rownames(summary(fm2.13)$coef)
)
)
Contrasts2
summary(glht(fm2.13, linfct=Contrasts2))
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