Produces a plot that compares the estimated trace line (item response function)
for an item with the data. The data is represented with a non-parametric
trace line computed as in tgf (not npp). Approximate confidence
intervals for the parametric line are also shown.
irfPlot(
resp,
ip,
x = NULL,
item,
main = "Parametric vs non-parametric trace line",
use.sm = FALSE
)A matrix of responses: persons as rows, items as columns, entries are either 0 or 1, no missing data
Item parameters: the object returned by function est --
note that the complete object is required since the standard errors play an important part.
A vector of abilities, as long as there are persons, against which to plot
probabilities of a correct response. Normally these would be the output of qrs,
and these are provided by default when x is NULL. However, the user may want to use
something else, or pass the same qrs output to several plots (various calls to
qrs provide slightly different output because of the random breaking of ties.)
The item for which a plot is requested (column of resp).
The main title of the plot.
When TRUE, empirical data will be shown with a regression estimated
by sm, otherwise the Bayes theorem will be used. Default is FALSE.
Comparing the outputs of npp and irf has at least two drawbacks:
(i) package sm is used as a black box, and (ii) the confidence intervals
are drawn around the data (even though represented by the nonparametric regression)
rather than the parametric curve. In this function, the parametric curve is shown in
red, with a 68
The non-parametric curve representing the data is shown in black. It is computed
by applying Bayes theorem to the density of ability.
The standard error of the parametric curve is computed by
applying the delta theorem on the standard error of the item parameters, as computed
by the underlying software (currently only available with ICL).
# NOT RUN {
# a plot for item 5
irfPlot(Scored, ip=b3, item=4)
# }
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