StroupGLMM (version 0.3.0)

Exam3.9: Example 3.9 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-118)

Description

Exam3.9 used to differentiate conditional and marginal binomial models with and without interaction for S2 variable.

Author

1. Muhammad Yaseen ()

2. Adeela Munawar ()

References

1. Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.

See Also

DataSet3.2

Examples

#-----------------------------------------------------------------------------------
## Binomial conditional GLMM without interaction, logit link
#-----------------------------------------------------------------------------------
library(MASS)
DataSet3.2$trt <- factor( x  =  DataSet3.2$trt )
DataSet3.2$loc <- factor( x  =  DataSet3.2$loc )

Exam3.9.fm1   <-
  glmmPQL(
      fixed    =  S2/Nbin~trt
    , random   = ~1|loc
    , family   =  quasibinomial(link = "logit")
    , data     =  DataSet3.2
    , niter    = 10
    , verbose  = TRUE
  )
summary(Exam3.9.fm1)
library(parameters)
model_parameters(Exam3.9.fm1)

#-------------------------------------------------------------
##  treatment means
#-------------------------------------------------------------
library(emmeans)
emmeans(object = Exam3.9.fm1, specs = ~trt, type = "response")
emmeans(object = Exam3.9.fm1, specs = ~trt, type = "link")
emmeans(object = Exam3.9.fm1, specs = ~trt, type = "logit")

##--- Normal Approximation
library(nlme)
Exam3.9fm2 <-
  lme(
      fixed       = S2/Nbin~trt
    , data        = DataSet3.2
    , random      = ~1|loc
    , method      = c("REML", "ML")[1]
  )

Exam3.9fm2
model_parameters(Exam3.9fm2)

emmeans(object  = Exam3.9fm2, specs = ~trt)


##---Binomial GLMM with interaction
Exam3.9fm3   <-
  glmmPQL(
      fixed       =  S2/Nbin~trt
    , random      = ~1|trt/loc
    , family      =  quasibinomial(link = "logit")
    , data        =  DataSet3.2
    , niter = 10
    , verbose = TRUE
  )
summary(Exam3.9fm3)
model_parameters(Exam3.9fm3)
emmeans(object = Exam3.9fm3, specs = ~trt)


##---Binomial Marginal GLMM(assuming compound symmetry)
Exam3.9fm4   <-
  glmmPQL(
      fixed       =  S2/Nbin~trt
    , random      = ~1|loc
    , family      =  quasibinomial(link = "logit")
    , data        =  DataSet3.2
    , correlation =  corCompSymm(form = ~1|loc)
    , niter       = 10
    , verbose     = TRUE
  )
summary(Exam3.9fm4)
model_parameters(Exam3.9fm4)
emmeans(object  = Exam3.9fm4, specs  = ~trt)