VerbAgg | R Documentation |
These are the item responses to a questionaire on verbal aggression. These data are used throughout De Boeck and Wilson, Explanatory Item Response Models (Springer, 2004) to illustrate various forms of item response models.
A data frame with 7584 observations on the following 13 variables.
Anger
the subject's Trait Anger score as measured on the State-Trait Anger Expression Inventory (STAXI)
Gender
the subject's gender - a factor with levels
M
and F
item
the item on the questionaire, as a factor
resp
the subject's response to the item - an ordered
factor with levels no
< perhaps
< yes
id
the subject identifier, as a factor
btype
behavior type - a factor with levels
curse
, scold
and shout
situ
situation type - a factor with levels
other
and self
indicating other-to-blame and self-to-blame
mode
behavior mode - a factor with levels want
and do
r2
dichotomous version of the response - a factor with
levels N
and Y
http://bear.soe.berkeley.edu/EIRM/
De Boeck and Wilson (2004), Explanatory Item Response Models, Springer.
str(VerbAgg) ## Show how r2 := h(resp) is defined: with(VerbAgg, stopifnot( identical(r2, { r <- factor(resp, ordered=FALSE); levels(r) <- c("N","Y","Y"); r}))) xtabs(~ item + resp, VerbAgg) xtabs(~ btype + resp, VerbAgg) round(100 * ftable(prop.table(xtabs(~ situ + mode + resp, VerbAgg), 1:2), 1)) person <- unique(subset(VerbAgg, select = c(id, Gender, Anger))) require(lattice) densityplot(~ Anger, person, groups = Gender, auto.key = list(columns = 2), xlab = "Trait Anger score (STAXI)") if(lme4:::testLevel() >= 3) { ## takes about 15 sec print(fmVA <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 + (1|id) + (1|item), family = binomial, data = VerbAgg), corr=FALSE) } ## much faster but less accurate print(fmVA0 <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 + (1|id) + (1|item), family = binomial, data = VerbAgg, nAGQ=0L), corr=FALSE)