Plots item characteristic curves — plotICC • eatModel

Function provides item characteristic plots for each item.

plotICC  ( resultsObj, defineModelObj, runModelObj = NULL, items = NULL,
       personPar = c("WLE", "EAP", "PV"), personsPerGroup = 30, pdfFolder = NULL,
       smooth = 7 )

Arguments

resultsObj: The object returned by getResults.
defineModelObj: The object returned by defineModel.
runModelObj: Optional: The object returned by runModel. Only necessary if ICCs for polytomous items should be plotted. Note: To date, this works ony for TAM.
items: Optional: A vector of items for which the ICC should be plotted. If NULL, ICCs of all items will be collected in a common pdf. The pdfFolder argument must not be NULL if the ICC of more than one item should be plotted, i.e. if items is not NULL or a vector of length > 1.
personPar: Which person parameter should be used for plotting? To mimic the behavior of the S3 plot method of TAM, use "WLE".
personsPerGroup: Specifies the number of persons in each interval of the theta scale for dividing the persons in various groups according to mean EAP score.
pdfFolder: Optional: A folder with writing access for the pdf file. Necessary only if ICCs for more than one item should be plotted.
smooth: Optional: A parameter (integer value) for smoothing the plot. If the number of examinees is high, the icc plot may become scratchy. smooth defines the maximum number of discrete nodes across the theta scale for evaluating the icc. Higher values result in a less smooth icc. To mimic the behavior of the S3 plot method of TAM, use the value 7.

Author

Sebastian Weirich

Examples

if (FALSE) { # \dontrun{
# This example estimates a TAM model before plotting. R CMD check skips it by
# default, but it can still be run locally via run_dontrun = TRUE.
data(trends)
# choose only 2010
dat <- trends[which(trends[,"year"] == 2010),]
# choose reading
dat <- dat[which(dat[,"domain"] == "reading"),]

# first reshape the data set into wide format
datW <- reshape2::dcast(dat, idstud~item, value.var="value")

# defining the model: specifying q matrix is not necessary
mod1 <- defineModel(dat=datW, items= -1, id="idstud", software = "tam")

# run the model
run1 <- runModel(mod1)

# get the results
res1 <- getResults(run1)

# plot for one item
plotICC  ( resultsObj = res1, defineModelObj = mod1, items = "T04_04")
} # }