Compute test information for a unidimensional IRT model (1PL/2PL/3PL) across a vector of ability values.
info(theta, ip, est = c("MLE", "EAP"), D = 1.702)A list with:
Vector of ability values.
If est = "MLE", vector of test information at each theta.
If est = "EAP", vector of test information at each theta.
Numeric vector of ability values at which to compute test information.
A data frame or matrix of item parameters. Columns are interpreted in order as:
3 columns: b, a, c (3PL, with a on the 1.702 metric),
2 columns: b, a (2PL, c internally set to 0),
1 column: b (1PL/Rasch, a = 1, c = 0).
Character string indicating the estimation method:
"MLE" for maximum likelihood or "EAP" for empirical Bayes.
A numeric constant representing the scaling factor of the IRT model.
Defaults to 1.702.
Test information at each \(\theta\) is the sum of item information.
For est = "EAP", this function returns
$$
I_{\mathrm{EAP}}(\theta) = I_{\mathrm{MLE}}(\theta) + 1,
$$
where the additional 1 reflects the prior (population) contribution under a
standard normal prior.