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]