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Package: |
MultiLCIRT |
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Type: |
Package |
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Version: |
2.11 |
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Date: |
2017-05-19 |
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License: |
GPL (>= 2) |
Function est_multi_poly performs the parameter estimation of the following IRT models,
allowing for one or more latent traits:
- Binary responses: Rasch model, 2-Parameter Logistic (2PL) model;
- Ordinal polythomous responses: Samejima's Graded Response Model (GRM) and
constrained versions with fixed
discrimination parameters and/or additive decomposition of difficulty parameters (rating scale
parameterization);
Muraki's Generalized Partial Credit Model and constrained versions with fixed discrimination parameters and/or
additive decomposition of difficulty parameters, such as Partial Credit Model and Rating Scale Model.
The basic input arguments for est_multi_poly are the person-item matrix of available response configurations
and the corresponding frequencies, the number of latent classes, the type of link function, the specification of
constraints on the discriminating and difficulty item parameters, and the allocation of items to the latent traits.
Missing responses are coded with NA, and units and items without responses are automatically removed.
Function test_dim performs a likelihood ratio test to choose the optimal number of latent traits (or
dimensions) by comparing nested models that differ in the number of latent traits, being all the other
elements let equal (i.e., number of latent classes, type of link function, constraints on item parameters).
The basic input arguments for test_dim are similar as those for est_multi_poly.
Function class_item performs a hierarchical clustering of items based on a specified LC IRT model.
The basic input arguments are given by the number of latent classes, the type of model, and the constraints
on the item parameters (only for polythomous responses).
An allocation of items to the different latent traits is obtained
depending on the cut-point of the resulting dendrogram.