#' MMvideo Mixed-Models
#'
#' http://www.stat.cmu.edu/~hseltman/309/Book/chapter15.pdf
#' A video game example
#' Consider a study of the learning effects of repeated plays of a video game where
#' age is expected to have an effect. The data are in MMvideo.txt. The quantitative
#' outcome is the score on the video game (in thousands of points). The explanatory
#' variables are age group of the subject and “trial” which represents which time the
#' subject played the game (1 to 5). The “id” variable identifies the subjects.
#'
#' \itemize{
#' \item id
#' \item agegrp
#' \item trial
#' \item score
#' }
#' @docType data
#' @keywords datasets
#' @name MMvideo
#' @usage data(MMvideo)
#' @format Ein data.frame mit 150 rows und 4 Variablen
#' @examples
#' library(stp25)
#' library(effects)
#' library(gridExtra)
#' #getwd()
#' # MMvideo<- read.table("C:/Users/wpete/Dropbox/3_Forschung/R-Project/stp25/extdata/MMvideo.txt",
#' # header=TRUE)
#' head(MMvideo)
#' #Projekt("html")
#' fit1<-lm(score ~ agegrp+trial, MMvideo)
#' fit2<-lmerTest::lmer(score ~ agegrp+trial + (1|id), MMvideo)
#' fit3<-lm(score ~ agegrp*trial, MMvideo)
#' fit4<-lmerTest::lmer(score ~ agegrp*trial + (1|id), MMvideo)
#' APA_Table(fit1, fit2, fit3, fit4, type="tex")
#'
#'
#'
#' # windows(8,6)
#' # p1 <- plot(effect("trial",fit2), multiline=TRUE)
#' # p2 <- plot(effect("agegrp*trial",fit4), multiline=TRUE)
#'
#' #grid.arrange(p1,p2,ncol=2)
#'
#' # library(coefplot)
#' # windows(4,3)
#' # coefplot(fit3, intercept=F, xlab="b (SE)")
#'
#' # windows(4,3)
#' # multiplot(fit1, fit2, intercept=F, xlab="b (SE)")
NULL
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