Examp2.5.4.1: Examp2.5.4.1 from Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Description

Examp2.5.4.1 is used for inspecting probability distribution and to define a plausible process through linear models and generalized linear models.

Author(s)

  1. Muhammad Yaseen (myaseen208@gmail.com)

References

  1. Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998).Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

See Also

ex124

Examples

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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 <- lme4::lmer(formula = Pcv ~ dose*Drug + (1|Region/Drug), data=ex125, REML = TRUE, 
                     contrasts = list(dose = "contr.SAS", Drug = "contr.SAS")
                     )
  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))
 

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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