Exam3.9 {StroupGLMM} | R Documentation |
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(s)
Muhammad Yaseen (myaseen208@gmail.com)
Adeela Munawar (adeela.uaf@gmail.com)
References
Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC Press.
See Also
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
# , weights
# , control
, niter = 10
, verbose = TRUE
# , ...
)
summary(Exam3.9.fm1)
#-------------------------------------------------------------
## treatment means
#-------------------------------------------------------------
library(lsmeans)
(Lsm3.9fm1 <-
lsmeans::lsmeans(
object = Exam3.9.fm1
, specs = "trt"
, link=TRUE
# , ...
)
)
##--- Normal Approximation
library(nlme)
Exam3.9fm2 <-
lme(
fixed = S2/Nbin~trt
, data = DataSet3.2
, random = ~1|loc
, weights = NULL
# , subset
, method = "REML" #c("REML", "ML")
, na.action = na.fail
# , control = list()
, contrasts = NULL
, keep.data = TRUE
)
(Lsm3.9fm2 <-
lsmeans::lsmeans(
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
# , weights
# , control
, niter = 10
, verbose = TRUE
# , ...
)
summary(Exam3.9fm3)
(Lsm3.9fm3 <-
lsmeans::lsmeans(
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)
# , weights
# , control
, niter = 10
, verbose = TRUE
# , ...
)
summary(Exam3.9fm4)
(Lsm3.9fm4 <-
lsmeans::lsmeans(
object = Exam3.9fm4
, specs = "trt"
# , ...
)
)
[Package StroupGLMM version 0.1.0 Index]