sim_subject_attributes {simcdm} | R Documentation |
Simulate Subject Latent Attribute Profiles \mathbf{\alpha}_c
Description
Generate a sample from the
\mathbf{\alpha}_c = (\alpha_{c1}, \ldots, \alpha_{cK})'
attribute profile matrix for members of class c
such that
\alpha_{ck}
' is 1 if members of class c
possess skill k
and zero otherwise.
Usage
sim_subject_attributes(N, K, probs = NULL)
Arguments
N |
Number of Observations |
K |
Number of Skills |
probs |
A |
Value
A N
by K
matrix
of latent classes
corresponding to entry c
of pi
based upon
mastery and nonmastery of the K
skills.
Author(s)
James Joseph Balamuta and Steven Andrew Culpepper
See Also
attribute_classes()
and attribute_inv_bijection()
Examples
# Define number of subjects and attributes
N = 100
K = 3
# Generate a sample from the Latent Attribute Profile (Alpha) Matrix
# By default, we sample from a uniform distribution weighting of classes.
alphas_builtin = sim_subject_attributes(N, K)
# Generate a sample using custom probabilities from the
# Latent Attribute Profile (Alpha) Matrix
probs = rep(1 / (2 ^ K), 2 ^ K)
alphas_custom = sim_subject_attributes(N, K, probs)
[Package simcdm version 0.1.2 Index]