1\docType{data} 2\name{VerbAgg} 3\alias{VerbAgg} 4\title{Verbal Aggression item responses} 5\format{A data frame with 7584 observations on the following 13 variables. 6 \describe{ 7 \item{\code{Anger}}{the subject's Trait Anger score as measured on 8 the State-Trait Anger Expression Inventory (STAXI)} 9 \item{\code{Gender}}{the subject's gender - a factor with levels 10 \code{M} and \code{F}} 11 \item{\code{item}}{the item on the questionaire, as a factor} 12 \item{\code{resp}}{the subject's response to the item - an ordered 13 factor with levels \code{no} < \code{perhaps} < \code{yes}} 14 \item{\code{id}}{the subject identifier, as a factor} 15 \item{\code{btype}}{behavior type - a factor with levels 16 \code{curse}, \code{scold} and \code{shout}} 17 \item{\code{situ}}{situation type - a factor with levels 18 \code{other} and \code{self} indicating other-to-blame and self-to-blame} 19 \item{\code{mode}}{behavior mode - a factor with levels \code{want} 20 and \code{do}} 21 \item{\code{r2}}{dichotomous version of the response - a factor with 22 levels \code{N} and \code{Y}} 23 }} 24\source{ 25 \url{http://bear.soe.berkeley.edu/EIRM/} 26} 27\description{ 28 These are the item responses to a questionaire on verbal 29 aggression. These data are used throughout De Boeck and 30 Wilson, \emph{Explanatory Item Response Models} 31 (Springer, 2004) to illustrate various forms of item 32 response models. 33} 34\examples{ 35str(VerbAgg) 36## Show how r2 := h(resp) is defined: 37with(VerbAgg, stopifnot( identical(r2, { 38 r <- factor(resp, ordered=FALSE); levels(r) <- c("N","Y","Y"); r}))) 39 40xtabs(~ item + resp, VerbAgg) 41xtabs(~ btype + resp, VerbAgg) 42round(100 * ftable(prop.table(xtabs(~ situ + mode + resp, VerbAgg), 1:2), 1)) 43person <- unique(subset(VerbAgg, select = c(id, Gender, Anger))) 44require(lattice) 45densityplot(~ Anger, person, groups = Gender, auto.key = list(columns = 2), 46 xlab = "Trait Anger score (STAXI)") 47 48if(lme4:::testLevel() >= 3) { ## takes about 15 sec 49 print(fmVA <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 + 50 (1|id) + (1|item), family = binomial, data = 51 VerbAgg), corr=FALSE) 52} ## testLevel() >= 3 53if (interactive()) { 54## much faster but less accurate 55 print(fmVA0 <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 + 56 (1|id) + (1|item), family = binomial, 57 data = VerbAgg, nAGQ=0L), corr=FALSE) 58} ## interactive() 59} 60\references{ 61 De Boeck and Wilson (2004), \emph{Explanatory Item 62 Response Models}, Springer. 63} 64\keyword{datasets} 65 66