VerbAgg {lme4} | R Documentation

## 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 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)

[Package lme4 version 1.1-12 Index]