plotICC: ICC Plots

Description

Plot functions for visualizing the item characteristic curves

Usage

## S3 method for class 'Rm':
plotICC(object, item.subset = "all", empICC = NULL, empCI = NULL,
   mplot = NULL, xlim = c(-4, 4), ylim = c(0, 1),
   xlab = "Latent Dimension", ylab = "Probability to Solve", main=NULL,
   col = NULL, lty = 1, legpos = "left", ask = TRUE, ...)
## S3 method for class 'dRm':
plotjointICC(object, item.subset = "all", legend = TRUE,
   xlim = c(-4, 4), ylim = c(0, 1), xlab = "Latent Dimension",
   ylab = "Probability to Solve", lty = 1, legpos = "topleft",
   main="ICC plot",col=NULL,...)

Arguments

object

object of class Rm or dRm

item.subset

Subset of items to be plotted. Either a numeric vector indicating the column in X or a character vector indiciating the column name. If "all" (default), all items are plotted.

empICC

Plotting the empirical ICCs for objects of class dRm. If empICC=NULL (the default) the empirical ICC is not drawn. Otherwise, empICC must be specified as a list where the first element must

empCI

Plotting confidence intervals for the the empirical ICCs. If empCI=NULL (the default) no confidence intervals are drawn. Otherwise, by specifying empCI as a list gives `exact' confidence intervals for e

mplot

if NULL the default setting is in effect. For models of class dRm this is mplot = TRUE, i.e., the ICCs for up to 4 items are plotted in one figure. For Rm models the default is

xlab

Label of the x-axis.

ylab

Label of the y-axis.

xlim

Range of person parameters.

ylim

Range for probability to solve.

legend

If TRUE, legend is provided, otherwise the ICCs are labeled.

col

If not specified or NULL, line colors are determined automatically. Otherwise, a scalar or vector with appropriate color specifications may be supplied (see par).

lty

Line type.

main

Title of the plot.

legpos

Position of the legend with possible values "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "rig

ask

If TRUE (the default) and the R session is interactive the user is asked for input, before a new figure is drawn. FALSE is only useful if automated figure export is in effect, e.g., when u

...

Additional plot parameters.

encoding

UTF-8

Details

Empirical ICCs for objects of class dRm can be plotted using the option empICC, a list where the first element specifies the type of calculation of the empirical values. If empICC=list("raw", other specifications) relative frequencies of the positive responses are calculated for each rawscore group and plotted at the position of the corresponding person parameter. The other options use the default versions of various smoothers: "tukey" (see smooth), "loess" (see loess), and "kernel" (see ksmooth). For "loess" and "kernel" a further element, smooth, may be specified to control the span (default is 0.75) or the bandwith (default is 0.5), respectively. For example, the specification could be empirical = list("loess", smooth=0.9) or empirical = list("kernel",smooth=2). Higher values result in smoother estimates of the empirical ICCs. The optional confidence intervals are obtained by a procedure first given in Clopper and Pearson (1934) based on the beta distribution (see binom.test).

Examples

Run this code

# Rating scale model, ICC plot for all items
rsm.res <- RSM(rsmdat)
thresholds(rsm.res)
plotICC(rsm.res)

# now items 1 to 4 in one figure without legends
plotICC(rsm.res, item.subset = 1:4, mplot = TRUE, legpos = FALSE)

# Rasch model for items 1 to 8 from raschdat1
# empirical ICCs displaying relative frequencies (default settings)
rm8.res <- RM(raschdat1[,1:8])
plotICC(rm8.res, empICC=list("raw"))

# the same but using different plotting styles
plotICC(rm8.res, empICC=list("raw",type="b",col="blue",lty="dotted"))

# kernel-smoothed empirical ICCs using bandwidth = 2
plotICC(rm8.res, empICC = list("kernel",smooth=3))

# raw empirical ICCs with confidence intervals
# displaying only items 2,3,7,8
plotICC(rm8.res, item.subset=c(2,3,7,8), empICC=list("raw"), empCI=list())

# Joint ICC plot for items 2, 6, 8, and 15 for a Rasch model
res <- RM(raschdat1)
plotjointICC(res, item.subset = c(2,6,8,15), legpos = "left")