dirf_p {lazy.irt}R Documentation

Calculation of the Derivatives of the Item Category Response Function with respect to Item Parameters

Description

Calculation of the Derivatives of the Item Category Response Function with respect to Item Parameters

Usage

dirf_p(paramj, theta = NULL, weight = NULL, smallP = 0, DinP = 1,
  thmin = -4, thmax = 4, npoints = 21, Pj = NULL, PPj = NULL,
  zero = 0, cat.first = 0, log = 0, print = 0)

Arguments

paramj

item parameters data frame for ONE item

theta

Discrete theta values

weight

Weight data frame: NOT used.

smallP

Minimum value of probability

DinP

= 1 to include D=1.7 in logistic function

thmin

Minimum value of discrete thata value

thmax

Maximum value of discrete thata value

npoints

# of discrete points for theta

Pj

icrf: npoints x (ncatj-1) (no zero category) or NULL

PPj

icbrf of the Graded Response Model or NULL

zero

= 1 to include the zero-th category in output

cat.first

= 1 to chage the category fist in the rows of Jack.

log

= 1 to obtain the log Jacobian: d log(ICRF) d param

print

= 1 to print result

Value

list of (Jack, Pj, PPj)
Jack (length(theta) x (ncatj-1)) x ncatj
derivative of vec(Pj) with respect to (a, b1, b2, ...)
If cat.first = 0
theta changes first, then k changes from 1 to ncatj
If cat.first = 1, category(k) changes first, then theta.
Pj Item Category Response Function (icrf) (length(theta) x (ncatj-1))
PPj will be output when type="G" or "Gn".

Examples

res=dirf_p( paramS1[3,], npoints=5, print=1 )
res=dirf_p( paramS1[3,], npoints=5, print=1, zero=1 )
res=dirf_p( paramS1[3,], npoints=5, print=1, cat.first=1 )



[Package lazy.irt version 0.1.3 Index]