fit223_ls {lazy.irt} R Documentation

## Conversion of 3PLM Items to 2PLM Items

### Description

Conversion of 3PLM Items to 2PLM Items

### Usage

```fit223_ls(param3, wtype = 0, wmean = 0, wsd = 1, DinP = 1,
npoints = 21, thmin = -3, thmax = 3, maxiter = 100, eps = 1e-06,
print = 1, plot = 0, debug = 0)
```

### Arguments

 `param3` Item Parameter Data Frame for 3PLM Items `wtype` = 1 to use dnorm(theta) as the weight `wmean` The mean of normal distribution to be used as the weight `wsd` The sd of normal distribution to be used as the weight `DinP` = 1 to include D=1.7 in logistic function `npoints` # of discrete points for theta `thmin` Minimum value of discrete thata value `thmax` Maximum value of discrete thata value `maxiter` Maximum # of GN iterations `eps` Convergence criterion for the relative improvement of rmse `print` = 1 to print result `plot` = 1 to plot result `debug` = 1 to print intemediate result

### Details

This function minimizes
` rss=sum( w*( vec(icrf_3(theta)) - vec(icrf_2(theta)) )^2 ) `
with respec to the 2PLM item parameters,
where `icrf_3(theta)` is the icrf of input 3PLM items and `icrf_2(theta)` is the icrf of fitted 2PLM items,
and `w` is the weight vector normal or 1.

Unlike fitP2G_ls and fitG2P_ls, the icrf of the 0-th category is not used.
Weighted Gauss-Newton method is used for the minimization.

### Value

A list of:
param3: Input 3PLM item parameter data frame (subset, type="P")
param2: 2PLM Item Parameter Data Frame (type="G")
```param2 <- fit223_ls( paramS2, plot=1, print=1 )