
isop.dich
function can be used for dichotomous
data while the isop.poly
function can be applied
to polytomous data. Note that for applying the ISOP model for
polytomous data it is necessary that all items do have the
same number of categories.isop.dich(dat, score.breaks = NULL, merge.extreme = TRUE,
conv = .0001, maxit = 1000, epsilon = .025, progress = TRUE)
isop.poly( dat , score.breaks=seq(0,1,len=10 ) ,
conv = .0001, maxit = 1000 , epsilon = .025 , progress=TRUE )
## S3 method for class 'isop':
summary(object,...)
## S3 method for class 'isop':
plot(x,ask=TRUE,...)
TRUE
.fit.adisop
isop
(generated by
isop.dich
or isop.poly
)isop
(generated by
isop.dich
or isop.poly
)isop.dich
)isop.poly
)fit.isop
)fit.adisop
)isop.dich
)isop.poly
)isop.dich
and isop.poly
uses $F^\ast$
from the ISOP models and estimates person and item parameters of the
ADISOP model. For comparison, isop.dich
also fits a model with
the logistic function $g$ which results in the Rasch
model.
For polytomous data, the starting point is the empirical
distribution function
fit.grm
:
person parameters $\theta_p$ (person.sc
),
item parameters $\delta_i$ (item.sc
) and
category parameters $\gamma_k$ (cat.sc
).
The calculation of person and item scores is explained
in isop.scoring
.
For an application of the ISOP and ADISOP model
see Scheiblechner and Lutz (2009).isop.scoring
,
fit.isop
and fit.adisop
.
Tests of the W1 axiom of the ISOP model (Scheiblechner, 1995) can be performed with
isop.test
.
See also the install.packages('ISOP',,'http://www.rforge.net/')
#############################################################################
# EXAMPLE 1: Dataset Reading (dichotomous items)
#############################################################################
data(data.read)
dat <- as.matrix( data.read)
I <- ncol(dat)
# Model 1: ISOP Model (11 score groups)
mod1 <- isop.dich( dat )
summary(mod1)
plot(mod1)
# Model 2: ISOP Model (5 score groups)
score.breaks <- seq( -.005 , 1.005 , len=5+1 )
mod2 <- isop.dich( dat , score.breaks=score.breaks)
summary(mod2)
#############################################################################
# EXAMPLE 2: Dataset PISA mathematics (dichotomous items)
#############################################################################
data(data.pisaMath)
dat <- data.pisaMath$data
dat <- dat[ , grep("M" , colnames(dat) ) ]
# fit ISOP model
# Note that for this model many iterations are needed
# to reach convergence for ADISOP
mod1 <- isop.dich( dat , maxit=4000)
summary(mod1)
#############################################################################
# EXAMPLE 3: Dataset Students (polytomous items)
#############################################################################
# Dataset students: scale cultural activities
library(CDM)
data(data.Students , package="CDM")
dat <- na.omit( data.Students[ , paste0("act",1:4) ] )
# fit models
mod1 <- isop.poly( dat )
summary(mod1)
plot(mod1)
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