np.dich: Nonparametric Estimation of Item Response Functions

In sirt: Supplementary Item Response Theory Models

Nonparametric Estimation of Item Response Functions

Description

This function does nonparametric item response function estimation (Ramsay, 1991).

Usage

np.dich(dat, theta, thetagrid, progress=FALSE, bwscale=1.1,
       method="normal")

Arguments

dat	An N \times I data frame of dichotomous item responses
theta	Estimated theta values, for example weighted likelihood estimates from wle.rasch
thetagrid	A vector of theta values where the nonparametric item response functions shall be evaluated.
progress	Display progress?
bwscale	The bandwidth parameter h is calculated by the formula h=bwscale\cdot N^{-1/5}
method	The default normal performs kernel regression with untransformed item responses. The method binomial uses nonparametric logistic regression implemented in the sm library.

Value

A list with following entries

dat	Original data frame
thetagrid	Vector of theta values at which the item response functions are evaluated
theta	Used theta values as person parameter estimates
estimate	Estimated item response functions
...

References

Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611-630.

Examples

#############################################################################
# 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 )

sirt documentation built on May 29, 2024, 8:43 a.m.