Description Author(s) References See Also Examples
Examp3.1 is.
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 3.1 Model 1 p-80
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=drug dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# ESTIMATE 'Mean Samorin' intercept 1 drug 0 1 dose(drug) 0 0 1;
# ESTIMATE 'Berenil 2 doses' dose(drug) 1 -1 0;
# ESTIMATE 'Ber vs Sam at dose 1' drug 1 -1 dose(drug) 1 0 -1;
# CONTRAST 'Mean Samorin' intercept 1 drug 0 1 dose(drug) 0 0 1;
# CONTRAST 'Berenil dif 2 doses' dose(drug) 1 -1 0;
# CONTRAST 'Ber vs Sam at dose 1' drug 1 -1 dose(drug) 1 0 -l;
# CONTRAST 'some difference' drug 1 -1 dose(drug) 0.5 0.5 -1,
# drug 0 0 dose(drug) 1 -1 0;
# LSMEANS dose(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.1 <-
lmerTest::lmer(
formula = PCV2 ~ drug1 + dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.1)
anova(object = fm3.1, ddf = "Satterthwaite")
lsmeansLT(model = fm3.1, test.effs = "dose1:drug1")
#-------------------------------------------------------------
## Example 3.1 Model 2 p-84
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=PCV1 drug dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.2 <-
lmerTest::lmer(
formula = PCV2 ~ PCV1 + drug1 + dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.2)
anova(object = fm3.2, ddf = "Satterthwaite")
lsmeansLT(model = fm3.2, test.effs = "herd1:drug1")
#-------------------------------------------------------------
## Example 3.1 Model 3 p-86
#-------------------------------------------------------------
# PROC MIXED DATA=ex31;
# CLASS drug dose herd;
# MODEL PCV2=drug dose(drug) PCV1*dose(drug)/solution ddfm=satterth;
# RANDOM herd(drug);
# RUN;
library(lmerTest)
str(ex31)
ex31$drug1 <- factor(ex31$drug)
ex31$dose1 <- factor(ex31$dose)
ex31$herd1 <- factor(ex31$herd)
fm3.3 <-
lmerTest::lmer(
formula = PCV2 ~ drug1 + PCV1*dose1:drug1 + (1|herd1:drug1)
, data = ex31
, REML = TRUE
, control = lmerControl()
, start = NULL
, verbose = 0L
# , subset
# , weights
# , na.action
# , offset
, contrasts = list(dose1 = "contr.SAS", drug1 = "contr.SAS")
, devFunOnly = FALSE
# , ...
)
summary(fm3.3)
anova(object = fm3.3, ddf = "Satterthwaite")
lsmeansLT(model = fm3.3, test.effs = "dose1:drug1")
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