calc_MI_FB: Calculate the mutual information using full Bayesian
In TestDesign: Optimal Test Design Approach to Fixed and Adaptive Test Construction
calc_MI_FB R Documentation
Calculate the mutual information using full Bayesian
Description
Calculate the mutual information using full Bayesian.
Usage
calc_MI_FB(x, items_list, ncat, model)
Arguments
x
A numeric vector of MCMC sampled theta values.
items_list
A list of item parameter matrices.
ncat
a numeric vector specifying the number of response categories in each item.
model
a numeric vector indicating the IRT models of each item (1: 1PL, 2: 2PL, 3: 3PL, 4: PC, 5: GPC, 6: GR).
TestDesign documentation built on Sept. 11, 2024, 7:12 p.m.
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| calc_MI_FB | R Documentation |
Calculate the mutual information using full Bayesian
Description
Calculate the mutual information using full Bayesian.
Usage
calc_MI_FB(x, items_list, ncat, model)
Arguments
x |
A numeric vector of MCMC sampled theta values. |
items_list |
A list of item parameter matrices. |
ncat |
a numeric vector specifying the number of response categories in each item. |
model |
a numeric vector indicating the IRT models of each item (1: 1PL, 2: 2PL, 3: 3PL, 4: PC, 5: GPC, 6: GR). |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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