smooth.ICC: 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)

Value

An S3 class ICC object for a single item.

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).

Author

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

Run this code

# example code to be set up

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