Exam7.1: Example 7.1 from Generalized Linear Mixed Models: Modern...

In myaseen208/StroupGLMM: R Codes and Datasets for Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup

Description

Exam7.1 explains multifactor models with all factors qualitative

Author(s)

1. Muhammad Yaseen ([e-mail])

2. Adeela Munawar ([e-mail])

References

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

See Also

DataSet7.1

Examples

library(lsmeans)
library(car)
data(DataSet7.1)

DataSet7.1$a <- factor(x = DataSet7.1$a)
DataSet7.1$b <- factor(x = DataSet7.1$b)

Exam7.1.lm1 <-
     lm(
        formula     = y ~ a + b + a*b
      , data        = DataSet7.1
    # , subset
    # , weights
    # , na.action
      , method      = "qr"
      , model       = TRUE
    # , x           = FALSE
    # , y           = FALSE
      , qr          = TRUE
      , singular.ok = TRUE
      , contrasts   = NULL
    # , offset
    # , ...
         )

summary( Exam7.1.lm1 )                                   
anova(Exam7.1.lm1)
##---Result obtained as in SLICE statement in SAS for a0 & a1 
library(phia)
a0 <- list(a=c("0"=1))
phia::testInteractions(Exam7.1.lm1, custom=a0, across="b")
a1 <- list(a=c("1"=1))
phia::testInteractions(Exam7.1.lm1, custom=a1, across="b")

 
##---Interaction plot
lsmip( 
       object  = Exam7.1.lm1
     , formula = a~b
     , ylab    = "y Lsmeans"
     , main    = "Lsmeans for a*b"
      )
#-------------------------------------------------------------
## Individula least squares treatment means 
#-------------------------------------------------------------
Lsm7.1 <-
     lsmeans::lsmeans(
         object  = Exam7.1.lm1
       , specs   = ~a*b
     # , ...
     )

Lsm7.1
##---Simpe effects comparison of interaction by a 
##   (but it doesn't give the same p-value as in article 7.4.2 page#215)
SimpleEff7.1 <-
 lsmeans::lsmeans(
         object  = Exam7.1.lm1
       , specs   = pairwise~b|a
     # , ...
     )$contrasts

SimpleEff7.1

##---Alternative method of pairwise comparisons by applying contrast
##   coefficient (gives the same p-value as in 7.4.2)
ContrastLsm7.1 <-
    lsmeans::contrast(
              Lsm7.1
            , list (
                    c1 = c(1,0,-1,0,0,0)
                  , c2 = c(1,0,0,0,-1,0)
                  , c3 = c(0,0,1,0,-1,0)
                  , c4 = c(0,1,0,-1,0,0)
                  , c5 = c(0,1,0,0,0,-1)
                  , c6 = c(0,1,0,0,-1,0)
                  ) 
      )
 
ContrastLsm7.1

##---Nested Model (page 216)----
Exam7.1.lm2 <-
     lm(
        formula     = y ~ a + a %in% b
      , data        = DataSet7.1
    # , subset
    # , weights
    # , na.action
      , method      = "qr"
      , model       = TRUE
    # , x           = FALSE
    # , y           = FALSE
      , qr          = TRUE
      , singular.ok = TRUE
      , contrasts   = NULL
    # , offset
    # , ...
   )

summary( Exam7.1.lm2 )                                   
anova(Exam7.1.lm2)

ContrastA0lm2 <- car::linearHypothesis(Exam7.1.lm2, c("a0:b1=a0:b2"))
ContrastA0lm2
ContrastA1lm2 <- car::linearHypothesis(Exam7.1.lm2,c("a1:b1=a1:b2"))
ContrastA1lm2

 ##---Bonferroni's adjusted p-values
SimpleEff7.1B <-
 lsmeans::lsmeans(
         object  = Exam7.1.lm2
       , specs   = pairwise~b|a
       , adjust  = "bonferroni"
     )$contrasts

SimpleEff7.1B

##---Alternative method of pairwise comparisons by applying contrast coefficient with Bonferroni adjustment
Bonferroni7.1 <-
    lsmeans::contrast(
          Lsm7.1
        , list(
            c1 = c(1,0,-1,0,0,0)
          , c2 = c(1,0,0,0,-1,0)
          , c3 = c(0,0,1,0,-1,0)
          , c4 = c(0,1,0,-1,0,0)
          , c5 = c(0,1,0,0,0,-1)
          , c6 = c(0,1,0,0,-1,0)
              )
        , adjust="bonferroni" 
         )
Bonferroni7.1

myaseen208/StroupGLMM documentation built on May 10, 2019, 8:28 a.m.