This function does nonparametric item response function estimation (Ramsay, 1991).
np.dich(dat, theta, thetagrid, progress=FALSE, bwscale=1.1,
method="normal")
An
Estimated theta values, for example weighted likelihood
estimates from wle.rasch
A vector of theta values where the nonparametric item response functions shall be evaluated.
Display progress?
The bandwidth parameter bwscale
The default normal
performs kernel regression
with untransformed item responses. The method binomial
uses nonparametric logistic regression implemented
in the sm library.
A list with following entries
Original data frame
Vector of theta values at which the item response functions are evaluated
Used theta values as person parameter estimates
Estimated item response functions
Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611-630.
# NOT RUN {
#############################################################################
# EXAMPLE 1: Reading dataset
#############################################################################
data( data.read )
dat <- data.read
# estimate Rasch model
mod <- sirt::rasch.mml2( dat )
# WLE estimation
wle1 <- sirt::wle.rasch( dat=dat, b=mod$item$b )$theta
# nonparametric function estimation
np1 <- sirt::np.dich( dat=dat, theta=wle1, thetagrid=seq(-2.5, 2.5, len=100 ) )
print( str(np1))
# plot nonparametric item response curves
plot( np1, b=mod$item$b )
# }
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