np.dich.Rd
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
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.
############################################################################# # 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 )