sirt: fit.isop – R documentation

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fit.isop

Fitting the ISOP and ADISOP Model for Frequency Tables

Description

Fit the isotonic probabilistic model (ISOP; Scheiblechner, 1995) and the additive isotonic probabilistic model (ADISOP; Scheiblechner, 1999).

Usage

fit.isop(freq.correct, wgt, conv=1e-04, maxit=100,
      progress=TRUE, calc.ll=TRUE)

fit.adisop(freq.correct, wgt, conv=1e-04, maxit=100,
      epsilon=0.01, progress=TRUE, calc.ll=TRUE)

Arguments

`freq.correct`	Frequency table
`wgt`	Weights for frequency table (number of persons in each cell)
`conv`	Convergence criterion
`maxit`	Maximum number of iterations
`epsilon`	Additive constant to handle cell frequencies of 0 or 1 in `fit.adisop`
`progress`	Display progress?
`calc.ll`	Calculate log-likelihood values? The default is `TRUE`.

Details

See isop.dich for more details of the ISOP and ADISOP model.

Value

A list with following entries

`fX`	Fitted frequency table
`ResX`	Residual frequency table
`fit`	Fit statistic: weighted least squares of deviations between observed and expected frequencies
`item.sc`	Estimated item parameters
`person.sc`	Estimated person parameters
`ll`	Log-likelihood of the model
`freq.fitted`	Fitted frequencies in a long data frame

Note

For fitting the ADISOP model it is recommended to first fit the ISOP model and then proceed with the fitted frequency table from ISOP (see Examples).

References

Scheiblechner, H. (1995). Isotonic ordinal probabilistic models (ISOP). Psychometrika, 60, 281-304.

Scheiblechner, H. (1999). Additive conjoint isotonic probabilistic models (ADISOP). Psychometrika, 64, 295-316.

Examples

#############################################################################
# EXAMPLE 1: Dataset Reading
#############################################################################

data(data.read)
dat <- as.matrix( data.read)
dat.resp <- 1 - is.na(dat) # response indicator matrix
I <- ncol(dat)

#***
# (1) Data preparation
#     actually only freq.correct and wgt are needed
#     but these matrices must be computed in advance.

# different scores of students
stud.p <- rowMeans( dat, na.rm=TRUE )
# different item p values
item.p <- colMeans( dat, na.rm=TRUE )
item.ps <- sort( item.p, index.return=TRUE)
dat <- dat[,  item.ps$ix ]
# define score groups students
scores <- sort( unique( stud.p ) )
SC <- length(scores)
# create table
freq.correct <- matrix( NA, SC, I )
wgt <- freq.correct
# percent correct
a1 <- stats::aggregate( dat==1, list( stud.p ), mean, na.rm=TRUE )
freq.correct <- a1[,-1]
# weights
a1 <- stats::aggregate( dat.resp, list( stud.p ), sum, na.rm=TRUE )
wgt <- a1[,-1]

#***
# (2) Fit ISOP model
res.isop <- sirt::fit.isop( freq.correct, wgt )
# fitted frequency table
res.isop$fX

#***
# (3) Fit ADISOP model
# use monotonely smoothed frequency table from ISOP model
res.adisop <- sirt::fit.adisop( freq.correct=res.isop$fX, wgt )
# fitted frequency table
res.adisop$fX

sirt

Supplementary Item Response Theory Models

v3.10-118

GPL (>= 2)

Authors

Alexander Robitzsch [aut,cre] (<https://orcid.org/0000-0002-8226-3132>)

Initial release

2021-09-22 17:45:34