Nonparametric Estimation of Item Response Functions
This function does nonparametric item response function estimation (Ramsay, 1991).
np.dich(dat, theta, thetagrid, progress=FALSE, bwscale=1.1,
method="normal")dat |
An N \times I data frame of dichotomous item responses |
theta |
Estimated theta values, for example weighted likelihood
estimates from |
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= |
method |
The default |
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 |
... |
Ramsay, J. O. (1991). Kernel smoothing approaches to nonparametric item characteristic curve estimation. Psychometrika, 56, 611-630.
############################################################################# # 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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