simdata: Simulated Rating Scale Data
In msd: Method of Successive Dichotomizations

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/msd.R

Generates simulated rating scale data given item measures, person measures and rating category thresholds.

1	simdata(items, persons, thresholds, missingProb = 0, minRating = 0)

`items`	a numeric vector of item measures with no NA.
`persons`	a numeric vector of person measures with no NA.
`thresholds`	a numeric vector of ordered rating category thresholds with no NA.
`missingProb`	a number between 0 and 1 specifying the probability of missing data.
`minRating`	integer representing the smallest ordinal rating category. Default is 0 (see Details).

It is assumed that the set of ordinal rating categories consists of all integers from the lowest rating category specified by minRating to the highest rating category, which is minRating + length(thresholds).

A numeric matrix of simulated rating scale data.

simdata can be used to test the accuracy of msd (see Examples).

Chris Bradley (cbradley05@gmail.com)

msd

# Use simdata to test the accuracy of msd. First, randomly generate item 
# measures, person measures and thresholds with 15 percent missing data and 
# ordinal rating categories from 0 to 5. Then, set mean item measure to zero 
# (axis origin in msd is the mean item measure) and mean threshold to zero 
# (any non-zero mean threshold is reflected in the person measures).
im <- runif(100, -2, 2)
pm <- runif(100, -2, 2)
th <- sort(runif(5, -2, 2))
im <- im - mean(im)
th <- th - mean(th)
d <- simdata(im, pm, th, missingProb = 0.15, minRating = 0)
m <- msd(d)

# Compare msd parameters to true values.  Linear regression should
# yield a slope very close to 1 and an intercept very close to 0.
lm(m$item_measures ~ im)
lm(m$person_measures ~ pm)
lm(m$thresholds ~ th)