| dirf_p {lazy.irtx} | R Documentation |
Calculation of the Derivative of Item Response Function with respect to Item Parameters
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)
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 xzero-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 |
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.
PPj will be output when type="G".
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 )