R/Exam2.B.7.R

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

#' @title Example 2.B.7 from Generalized Linear Mixed Models: Modern Concepts, Methods and Applications by Walter W. Stroup(p-60)
#' @name   Exam2.B.7
#' @description Exam2.B.7 is related to multi batch regression data assuming different forms of linear models with factorial experiment.
#' @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{DataExam2.B.7}}
#'
#' @import parameters
#' @importFrom stats lm summary.lm model.matrix lm.fit coef
#'
#' @examples
#' #-----------------------------------------------------------------------------------
#' ## Classical main effects and Interaction Model
#' #-----------------------------------------------------------------------------------
#' data(DataExam2.B.7)
#' DataExam2.B.7$a <- factor(x = DataExam2.B.7$a)
#' DataExam2.B.7$b <- factor(x = DataExam2.B.7$b)
#' Exam2.B.7.lm1 <- lm(formula = y~ a + b + a*b, data = DataExam2.B.7)
#' #-----------------------------------------------------------------------------------
#' ## One way treatment effects model
#' #-----------------------------------------------------------------------------------
#' DesignMatrix.lm1 <- model.matrix (object = Exam2.B.7.lm1)
#' DesignMatrix2.B.7.2 <- DesignMatrix.lm1[,!colnames(DesignMatrix.lm1) %in% c("a2","b")]
#'
#' lmfit2 <- lm.fit(x = DesignMatrix2.B.7.2, y = DataExam2.B.7$y)
#' Coefficientslmfit2 <- coef( object = lmfit2)
#' Coefficientslmfit2
#'
#' #-----------------------------------------------------------------------------------
#' ## One way treatment effects model without intercept
#' #-----------------------------------------------------------------------------------
#' DesignMatrix2.B.7.3    <-
#'   as.matrix(DesignMatrix.lm1[,!colnames(DesignMatrix.lm1) %in% c("(Intercept)","a2","b")])
#'
#' lmfit3 <- lm.fit(x = DesignMatrix2.B.7.3, y = DataExam2.B.7$y)
#' Coefficientslmfit3 <- coef( object = lmfit3)
#' Coefficientslmfit3
#'
#' #-----------------------------------------------------------------------------------
#' ## Nested Model (both models give the same result)
#' #-----------------------------------------------------------------------------------
#' Exam2.B.7.lm4 <- lm(formula = y~ a + a/b, data  = DataExam2.B.7)
#' summary(Exam2.B.7.lm4)
#'
#' Exam2.B.7.lm4 <- lm(formula = y~ a + a*b, data = DataExam2.B.7)
#' summary(Exam2.B.7.lm4)
#'
NULL