#' @title Example 5.1 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-163)
#' @name Exam5.1
#' @docType data
#' @keywords datasets
#' @description Exam5.1 is used to show polynomial multiple regression with binomial response
#' @author \enumerate{
#' \item Muhammad Yaseen (\email{myaseen208@@gmail.com})
#' \item Adeela Munawar (\email{adeela.uaf@@gmail.com})
#' }
#' @references \enumerate{
#' \item Stroup, W. W. (2012).
#' \emph{Generalized Linear Mixed Models: Modern Concepts, Methods and Applications}.
#' CRC Press.
#' }
#' @seealso
#' \code{\link{DataSet5.1}}
#'
#' @importFrom aod wald.test
#' @importFrom stats lm summary.lm glm summary.glm anova
#'
#' @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
#' )
NULL
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