Power_plot {TestGardener}

Plot item power curves for selected items or questions.

Description

Item surprisal power curves are the square root of the sum over options of the squared surprisal sensitivity curves.

Usage

  Power_plot(scrfine, SfdList, Qvec, dataList, plotindex=1:n, 
             plotrange=c(min(scrfine),max(scrfine)), height=0.5,  
             value=0, ttlsz=NULL, axisttl=NULL, axistxt=NULL)

Arguments

scrfine

A vector of length nfine (usually 101) containing equally spaced points spanning the plotrange. Used for plotting.

SfdList

A numbered list object produced by a TestGardener analysis of a test. Its length is equal to the number of items in the test or questions in the scale. Each member of SfdList is a named list containing information computed during the analysis.

Qvec

The five marker percentile values.

dataList

A list vector containing objects essential to an analysis.

plotindex

A set of integers specifying the numbers of the items or questions to be displayed.

plotrange

A vector of length 2 containing the plot boundaries within or over the score index interval c(0,100).

height

A positive real number defining the upper limit on the ordinate for the plots.

value

Number required by ggplot2. Defaults to 0.

ttlsz

Title font size.

axisttl

Axis title font size.

axistxt

Axis text(tick label) font size.

Details

A surprisal power curve for each question indexed in the index argument. A request for a keystroke is made for each question. The answer to question strongly defines the optimal position of an estimated score index value where the curve is high value. Values of power curves typically range over [0,0.5].

Value

The plots of the power curves specified in plotindex are produced as a side effect. If saveplot is TRUE, the plots of item power curves specified in plotindex are bundled into a single postscript or .pdf file and the file name is defined by paste(dataList$titlestr,i,'-power.pdf',sep=""). The file is then outputas a returned value.

Author(s)

Juan Li and James Ramsay

References

Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297-315.

Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with information-based psychometrics. Psych, 2, 347-360.

See Also

Sensitivity_plot, Entropy_plot, Ffuns_plot, ICC_plot

Examples

#  Example 1.  Display the item power curves for the 
#  short SweSAT multiple choice test with 24 items and 1000 examinees
#  plot the power curve for the first item
dataList <- Quant_13B_problem_dataList
SfdList  <- Quant_13B_problem_parmList$SfdList
Qvec     <- Quant_13B_problem_parmList$Qvec
scrfine <- seq(0,100,len=101)
oldpar <- par(no.readonly=TRUE)
Power_plot(scrfine, SfdList, Qvec, dataList, plotindex=1)
par(oldpar)