Exam5.1: Example 5.1 from Generalized Linear Mixed Models: Modern...

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

Description

Exam5.1 is used to show polynomial multiple regression with binomial response

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

DataSet5.1

Examples

##---Sequential Fit of the logit Model
Exam5.1.glm.1 <-
  glm(
      formula    =  F/N~ X
    , family     =  quasibinomial(link = "logit")
    , data       =  DataSet5.1
    , weights    =  NULL
 #  , subset
 #  , na.action
    , start      =  NULL
 #  , etastart
 #  , mustart
 #  , offset
 #  , control    =  list(...)
 #  , model      =  TRUE
    , method     =  "glm.fit"
 #  , x          =  FALSE
 #  , y          =  TRUE
    , contrasts  =  NULL
 #  , ...
  )
summary(Exam5.1.glm.1)

## confint.default()   produce Wald Confidence interval as SAS produces
##---Likelihood Ratio test for Model 1
(LRExam5.1.glm.1  <-
  anova(
      object =  Exam5.1.glm.1
    , test   = "Chisq")
)

library(aod)
WaldExam5.1.glm.1 <-
  wald.test(
      Sigma   = vcov(object=Exam5.1.glm.1)
    , b       = coef(object=Exam5.1.glm.1)
    , Terms   = 2
    , L       = NULL
    , H0      = NULL
    , df      = NULL
    , verbose = FALSE
  )

##---Sequential Fit of the logit Model quadratic terms involved
Exam5.1.glm.2 <-
  glm(
      formula    =  F/N~ X + I(X^2)
    , family     =  quasibinomial(link = "logit")
    , data       =  DataSet5.1
    , weights    =  NULL
 #  , subset
 #  , na.action
    , start      =  NULL
 #  , etastart
 #  , mustart
 #  , offset
 #  , control    =  list(...)
 #  , model      =  TRUE
    , method     =  "glm.fit"
 #  , x          =  FALSE
 #  , y          =  TRUE
    , contrasts  =  NULL
 #  , ...
  )
summary( Exam5.1.glm.2 )

##---Likelihood Ratio test for Model Exam5.1.glm.2
(LRExam5.1.glm.2   <-
  anova(
      object =  Exam5.1.glm.2
    , test   = "Chisq")
)

WaldExam5.1.glm.2 <-
  wald.test(
      Sigma   = vcov(object=Exam5.1.glm.2)
    , b       = coef(object=Exam5.1.glm.2)
    , Terms   = 3
    , L       = NULL
    , H0      = NULL
    , df      = NULL
    , verbose = FALSE
  )
  
##---Sequential Fit of the logit Model 5th power terms involved
Exam5.1.glm.3 <-
  glm(
      formula    =  F/N~ X + I(X^2) + I(X^3) + I(X^4) + I(X^5)
    , family     =  quasibinomial(link = "logit")
    , data       =  DataSet5.1
    , weights    =  NULL
 #  , subset
 #  , na.action
    , start      =  NULL
 #  , etastart
 #  , mustart
 #  , offset
 #  , control    =  list(...)
 #  , model      =  TRUE
    , method     =  "glm.fit"
 #  , x          =  FALSE
 #  , y          =  TRUE
    , contrasts  =  NULL
 #  , ...
  )
summary(Exam5.1.glm.3)

## confint.default()   produce Wald Confidence interval as SAS produces
##---Likelihood Ratio test for Model 1
(LRExam5.1.glm.3   <-
  anova(
      object =  Exam5.1.glm.3
    , test   = "Chisq")
)

WaldExam5.1.glm.3 <-
  wald.test(
      Sigma   = vcov(object=Exam5.1.glm.3)
    , b       = coef(object=Exam5.1.glm.3)
    , Terms   = 6
    , L       = NULL
    , H0      = NULL
    , df      = NULL
    , verbose = FALSE
  )

StroupGLMM documentation built on May 2, 2019, 9:42 a.m.