fit223_ls {lazy.irt} | R Documentation |

Conversion of 3PLM Items to 2PLM Items

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 )

`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 |

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.

A list of:

param3: Input 3PLM item parameter data frame (subset, type="P")

param2: 2PLM Item Parameter Data Frame (type="G")

rmse: Vector of sqrt(rss/length(theta)) for each item.
grad: Gradient matrix

wtype, wmean, wsd

param2 <- fit223_ls( paramS2, plot=1, print=1 ) param21 <- fit223_ls( paramS2, plot=1, print=1, wtype=1 )

[Package *lazy.irt* version 0.1.3 Index]