R: Simulate Subject Latent Attribute Profiles \mathbf{alpha}_c

sim_subject_attributes {simcdm} | R Documentation

## Simulate Subject Latent Attribute Profiles \mathbf{α}_c

### Description

Generate a sample from the \mathbf{α}_c = (α_{c1}, …, α_{cK})' attribute profile matrix for members of class c such that α_{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 `vector` of probabilities that sum to 1.

### 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

`simcdm::attribute_classes()` and `simcdm::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 0.1.1 Index]