Description Usage Arguments Details Value Author(s) See Also Examples
Simulate data from polytomous logit-normit (graded logistic) model
1 | simulpln(n, nitem, ncat, alphas, betas)
|
n |
Number of responses to generate. |
nitem |
Number of items. |
ncat |
Number of categories for the items. |
alphas |
A vector of length |
betas |
A vector of length |
Data from graded logistic models is generated under the following parameterization:
Pr(y_i = k_i| η) = { 1-Ψ (α_i,k + β_i*η) if k_i = 0, Ψ (α_i,k + β_i*η) - Ψ (α_i,k+1 + β_i*η) if 0 < k_i < m-1, Ψ (α_i,k+1 + β_i*η) if k_i = m-1}.
Where the items are y_i, i = 1, …, n, and response categories are k=0, …, m-1. η is the latent trait, Ψ is the logistic distribution function, α is an intercept (cutpoint) parameter, and β is a slope parameter. When the number of categories for the items is 2, this reduces to the 2PL parameterization:
Pr(y_i = 1| η) = Ψ (α_1 + β_i η)
A data matrix in which each row represents a response pattern and the final column represents the frequency of each response pattern.
Carl F. Falk cffalk@gmail.com, Harry Joe
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