| scorePerformance | R Documentation |
Description
After the simulated data matrices have been
analyzed, prepare the objects necessary for the
performance plots produced by functions RMSEbias1.plot and
RMSEbias2.plot.
Usage
scorePerformance(dataList, simList)
Arguments
dataList |
A list that contains the objects needed to analyse the test
or rating scale with the following fields:
- chcemat:
A matrix of response data with N rows and n columns where
N is the number of examinees or respondents and n is the number of items.
Entries in the matrices are the indices of the options chosen.
Column i of chcemat is expected to contain only the integers
1,...,noption.
- optList:
A list vector containing the numerical score values
assigned to the options for this question.
- key:
If the data are from a test of the multiple choices type
where the right answer is scored 1 and the wrong answers 0, this is
a numeric vector of length n containing the indices the right answers.
Otherwise, it is NULL.
- Sfd:
An fd object for the defining the surprisal curves.
- noption:
A numeric vector of length n containing the numbers of
options for each item.
- nbin:
The number of bins for binning the data.
- scrrng:
A vector of length 2 containing the limits of observed
sum scores.
- scrfine:
A fine mesh of test score values for plotting.
- scrvec:
A vector of length N containing the examinee or
respondent sum scores.
- itemvec:
A vector of length n containing the question or item
sum scores.
- percntrnk:
A vector length N containing the sum score
percentile ranks.
- chcematQnt:
A numeric vector of length 2*nbin + 1 containing the
bin boundaries alternating with the bin centers. These are initially
defined as seq(0,100,len=2*nbin+1).
- Sdim:
The total dimension of the surprisal scores.
- PcntMarkers:
The marker percentages for plotting:
5, 25, 50, 75 and 95.
|
simList |
A named list containing these objects:
- sumscr:
A matrix with row dimension nchcemat, the number of
population score index values and column dimension nsample, the
number of simulated samples.
- chcemat:
An nchcemat by nsample of estimated score
index values.
- mu:
An nchcemat by nsample of estimated expected
score values.
- al:
An nchcemat by nsample of estimated test
information curve values.
- thepop:
A vector of population score index values.
- mupop:
A vector of expected scores computed from the population
score index values.
- alpop:
A vector of test information values computed from the
population score index values.
- n:
The number of questions.
- Qvec:
The five marker percentile values.
|
Value
A named list containing these objects:
- sumscr:
A matrix with row dimension nchcemat, the number of
population score index values and column dimension nsample, the
number of simulated samples.
- chcemat:
An nchcemat by nsample matrix of estimated score
index values.
- mu:
An nchcemat by nsample matrix of estimated expected
score values.
- al:
An nchcemat by nsample matrix of estimated test
information curve values.
- chcepop:
A vector of population score index values.
- mupop:
A vector of expected scores computed from the population
score index values.
- infopop:
A vector of test information values computed from the
population score index values.
- n:
The number of questions.
- Qvec:
The five marker percentile values.
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
dataSimulation
