dirf {lazy.irt} | R Documentation |

Calculation of Derivative of Item Response Function

dirf(param, theta = NULL, weight = NULL, zero = 1, smallP = 1e-09, thmin = -4, thmax = 4, npoints = 21, DinP = 1, numderiv = 0, eps = 1e-06, log = 0, print = 1, debug = 0, plot = 0)

`param` |
Item Parameter Data Frame |

`theta` |
Discrete theta values |

`weight` |
Weight data frame |

`zero` |
= 0 to exclude the xzero-th category from output |

`smallP` |
Minimum value of probability |

`thmin` |
Minimum value of discrete thata value |

`thmax` |
Maximum value of discrete thata value |

`npoints` |
# of discrete points for theta |

`DinP` |
= 1 to include D=1.7 in logistic function |

`numderiv` |
= 1 to use numerical derivatives |

`eps` |
eps for JacobianMat |

`log` |
= 1 to calculate log derivatives |

`print` |
= 1 to print result |

`debug` |
= 1 to print intemediate result |

`plot` |
= 1 to plot result |

A list of

dICRF, dIRF, dTRF, fromP, toP=toP, vecv, minscore_i, maxscore_i,
minscore_t, maxscore_tt, log

where

dICRF npoints x sum(ncat)

dIRF npoints x nitems weighted by item category weight

dTRF npoints x 1 weighted by item category weight

and item weight

fromP, toP location of each item category in dICRF

vectorize category weights

minscore_i mimimum score of each item

maxscore_i maximum score of each item

minscore_t mimimum score of test

maxscore_t maximum score of test

Note that when log=1, dICRF etc are the log derivatives, namely,
the derivative of log ICRF w.r.t. theta, etc.

dirf( paramS1, plot=1 ) # compare analytic and numeric derivative res1=dirf( paramS1, print=0, plot=0 )$dICRF res2=dirf( paramS1, print=0, numderiv=1, plot=0 )$dICRF Print(max(abs(res1-res2)))

[Package *lazy.irt* version 0.1.3 Index]