simcdm (version 0.1.2)

sim_subject_attributes: 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)

Value

A \(N\) by \(K\)

matrix of latent classes corresponding to entry \(c\) of \(pi\) based upon mastery and nonmastery of the \(K\) skills.

Arguments

N

Number of Observations

K

Number of Skills

probs

A vector of probabilities that sum to 1.

Author

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)