#' @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
#' @docType data
#' @keywords datasets
#' @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}}
#'
#' @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
#' # , subset
#' # , weights
#' # , na.action
#' , method = "qr"
#' , model = TRUE
#' # , x = FALSE
#' # , y = FALSE
#' , qr = TRUE
#' , singular.ok = TRUE
#' , contrasts = NULL
#' # , offset
#' # , ...
#' )
#' #-----------------------------------------------------------------------------------
#' ## 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$y
#' , offset = NULL
#' , method = "qr"
#' , tol = 1e-07
#' , singular.ok = TRUE
#' # , ...
#' )
#' Coefficientslmfit2 <- coef( object = lmfit2)
#' #-----------------------------------------------------------------------------------
#' ## One way treatment effects model without intercept
#' #-----------------------------------------------------------------------------------
#' DesignMatrix2.B.7.3 <-
#' as.matrix(DesignMatrix.lm1[,!colnames(DesignMatrix.lm1) %in% c("(Intercept)","a2","b")])
#'
#' lmfit3 <-
#' lm.fit(
#' x = DesignMatrix2.B.7.3
#' , y = DataExam2.B.7$y
#' , offset = NULL
#' , method = "qr"
#' , tol = 1e-07
#' , singular.ok = TRUE
#' # , ...
#' )
#' Coefficientslmfit3 <- coef( object = lmfit3)
#'
#' #-----------------------------------------------------------------------------------
#' ## Nested Model (both models give the same result)
#' #-----------------------------------------------------------------------------------
#' Exam2.B.7.lm4 <-
#' lm(
#' formula = y~ a + a/b
#' , data = DataExam2.B.7
#' # , subset
#' # , weights
#' # , na.action
#' , method = "qr"
#' , model = TRUE
#' # , x = FALSE
#' # , y = FALSE
#' , qr = TRUE
#' , singular.ok = TRUE
#' , contrasts = NULL
#' # , offset
#' # , ...
#' )
#' summary(Exam2.B.7.lm4)
#'
#' Exam2.B.7.lm4 <-
#' lm(
#' formula = y~ a + a*b
#' , data = DataExam2.B.7
#' # , subset
#' # , weights
#' # , na.action
#' , method = "qr"
#' , model = TRUE
#' # , x = FALSE
#' # , y = FALSE
#' , qr = TRUE
#' , singular.ok = TRUE
#' , contrasts = NULL
#' # , offset
#' # , ...
#' )
#' summary(Exam2.B.7.lm4)
NULL
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