R Dataset / Package lme4 / VerbAgg

On this R-data statistics page, you will find information about the VerbAgg data set which pertains to Verbal Aggression item responses. The VerbAgg data set is found in the lme4 R package. You can load the VerbAgg data set in R by issuing the following command at the console data("VerbAgg"). This will load the data into a variable called VerbAgg. If R says the VerbAgg data set is not found, you can try installing the package by issuing this command install.packages("lme4") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the VerbAgg R data set. The size of this file is about 437,902 bytes.

Verbal Aggression item responses

Description

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.

Format

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

Source

http://bear.soe.berkeley.edu/EIRM/

References

De Boeck and Wilson (2004), Explanatory Item Response Models, Springer.

Examples

str(VerbAgg)
## Show howr2 := 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)

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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