R: Generate data from the rRUM

sim_rrum_items {simcdm} | R Documentation

## Generate data from the rRUM

### Description

Randomly generate response data according to the reduced Reparameterized Unified Model (rRUM).

### Usage

    sim_rrum_items(Q, rstar, pistar, alpha)

### Arguments

`Q` | A `matrix` with J rows and K columns indicating which attributes are required to answer each of the items, where J represents the number of items and K the number of attributes. An entry of 1 indicates attribute k is required to answer item j. An entry of one indicates attribute k is not required.
`rstar` | A `matrix` a matrix with J rows and K columns indicating the penalties for failing to have each of the required attributes, where J represents the number of items and K the number of attributes. `rstar` and `Q` must share the same 0 entries.
`pistar` | A `vector` of length J indicating the probabiliies of answering each item correctly for individuals who do not lack any required attribute, where J represents the number of items.
`alpha` | A `matrix` with N rows and K columns indicating the subjects attribute acquisition, where K represents the number of attributes. An entry of 1 indicates individual i has attained attribute k. An entry of 0 indicates the attribute has not been attained.

### Value

Y A `matrix` with N rows and J columns indicating the indviduals' responses to each of the items, where J represents the number of items.

### Author(s)

Steven Andrew Culpepper, Aaron Hudson, and James Joseph Balamuta

### References

Culpepper, S. A. & Hudson, A. (In Press). An improved strategy for Bayesian estimation of the reduced reparameterized unified model. Applied Psychological Measurement.

Hudson, A., Culpepper, S. A., & Douglas, J. (2016, July). Bayesian estimation of the generalized NIDA model with Gibbs sampling. Paper presented at the annual International Meeting of the Psychometric Society, Asheville, North Carolina.

### Examples

    # Set seed for reproducibility
    set.seed(217)

    # Define Simulation Parameters
    N = 1000 # number of individuals
    J = 6 # number of items
    K = 2 # number of attributes

    # Matrix where rows represent attribute classes
    As = attribute_classes(K)

    # Latent Class probabilities
    pis = c(.1, .2, .3, .4)

    # Q Matrix
    Q = rbind(c(1, 0),
              c(0, 1),
              c(1, 0),
              c(0, 1),
              c(1, 1),
              c(1, 1)
        )

    # The probabiliies of answering each item correctly for individuals
    # who do not lack any required attribute
    pistar = rep(.9, J)

    # Penalties for failing to have each of the required attributes
    rstar  = .5 * Q

    # Randomized alpha profiles
    alpha  = As[sample(1:(K ^ 2), N, replace = TRUE, pis),]

    # Simulate data
    rrum_items = sim_rrum_items(Q, rstar, pistar, alpha)

[Package simcdm version 0.1.1 Index]