smooth.ICC: Smooth binned probability and surprisal values to make an...

In TestGardener: Information Analysis for Test and Rating Scale Data

Smooth binned probability and surprisal values to make an ICC object.

Description

An N by n matrix of positive integer choice index values is transformed to an nbin by M matrix of probability values by iteravely minimizing the sum of squared errors for bin values.

Usage

smooth.ICC(x, item, index, dataList, indexQnt=seq(0,100, len=2*nbin+1), 
                       wtvec=matrix(1,n,1), iterlim=20, conv=1e-4, dbglev=0)

Arguments

x	An ICC object
item	Index of item being set up.
index	A vector of length N containing score index values for each person.
dataList	A list object set up by function make.dataList containing objects set up prior to an analysis of the data.
indexQnt	A vector of length 2*nbin + 1 containing, in sequence, the lower boundary of a bin, its midpoint, and the upper boundary.
wtvec	A vector of length n containing wseights for items.
iterlim	An integer specifying the maximum number of optimizations.
conv	A convergence criterion a little larger than 0.
dbglev	One of integers 0 (no optimization information), 1 (one line per optimization) or 2 (complete optimization display).

Value

An S3 class ICC object for a single item.

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.

Examples

# example code to be set up

TestGardener documentation built on Nov. 24, 2023, 5:08 p.m.