Nothing
#' @title Example 7.1 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup (p-213)
#' @name Exam7.1
#' @description Exam7.1 explains multifactor models with all factors qualitative
#' @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{DataSet7.1}}
#'
#' @import parameters
#' @import emmeans
#' @importFrom phia testInteractions
#' @importFrom car linearHypothesis
#'
#' @examples
#'
#' library(emmeans)
#' 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)
#' summary(Exam7.1.lm1)
#' library(parameters)
#' model_parameters(Exam7.1.lm1)
#' anova(Exam7.1.lm1)
#'
#' ##---Result obtained as in SLICE statement in SAS for a0 & a1
#' library(phia)
#' testInteractions(
#' model = Exam7.1.lm1
#' , custom = list(a = c("0" = 1))
#' , across = "b"
#' )
#'
#' testInteractions(
#' model = Exam7.1.lm1
#' , custom = list(a = c("1" = 1))
#' , across = "b"
#' )
#'
#'
#' ##---Interaction plot
#' emmip(
#' object = Exam7.1.lm1
#' , formula = a~b
#' , ylab = "y Lsmeans"
#' , main = "Lsmeans for a*b"
#' )
#'
#' #-------------------------------------------------------------
#' ## Individula least squares treatment means
#' #-------------------------------------------------------------
#' emmeans(object = Exam7.1.lm1, specs = ~a*b)
#'
#' ##---Simpe effects comparison of interaction by a
#' ## (but it doesn't give the same p-value as in article 7.4.2 page#215)
#' emmeans(object = Exam7.1.lm1, specs = pairwise~b|a)$contrasts
#'
#' pairs(emmeans(object = Exam7.1.lm1, specs = ~b|a), simple = "each", combine = TRUE)
#' pairs(emmeans(object = Exam7.1.lm1, specs = ~b|a), simple = "a")
#' pairs(emmeans(object = Exam7.1.lm1, specs = ~b|a), simple = "b")
#' pairs(emmeans(object = Exam7.1.lm1, specs = ~b|a))
#' contrast(emmeans(object = Exam7.1.lm1, specs = ~b|a))
#' emmeans(object = Exam7.1.lm1, specs = pairwise~b|a)
#' emmeans(object = Exam7.1.lm1, specs = pairwise~b|a)$contrasts
#'
#' ##---Alternative method of pairwise comparisons by
#' ## applying contrast
#' ## coefficient (gives the same p-value as in 7.4.2)
#' contrast(
#' emmeans(object = Exam7.1.lm1, specs = ~a*b)
#' , 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)
#' )
#' )
#'
#'
#' ##---Nested Model (page 216)----
#' Exam7.1.lm2 <- lm(formula = y ~ a + a %in% b, data = DataSet7.1)
#'
#' summary(Exam7.1.lm2)
#' model_parameters(Exam7.1.lm2)
#' anova(Exam7.1.lm2)
#'
#' car::linearHypothesis(Exam7.1.lm2, c("a0:b1 = a0:b2"))
#' car::linearHypothesis(Exam7.1.lm2, c("a1:b1 = a1:b2"))
#'
#' ##---Bonferroni's adjusted p-values
#' emmeans(object = Exam7.1.lm2, specs = pairwise~b|a, adjust = "bonferroni")$contrasts
#'
#' ##--- Alternative method of pairwise comparisons by
#' ## applying contrast coefficient with Bonferroni adjustment
#' contrast(
#' emmeans(object = Exam7.1.lm1, specs = ~a*b)
#' , 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"
#' )
#'
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.