rpf.sample: Randomly sample response patterns given a list of items

Description

Returns a random sample of response patterns given a list of item models and parameters. If grp is given then theta, items, params, mean, and cov can be omitted.

Usage

rpf.sample(
  theta,
  items,
  params,
  ...,
  prefix = "i",
  mean = NULL,
  cov = NULL,
  mcar = 0,
  grp = NULL
)

Value

Returns a data frame of response patterns

Arguments

theta: either a vector (for 1 dimension) or a matrix (for >1 dimension) of person abilities or the number of response patterns to generate randomly
items: a list of item models
params: a list or matrix of item parameters. If omitted, random item parameters are generated for each item model.
...: Not used. Forces remaining arguments to be specified by name.
prefix: Column names are taken from param or items. If no column names are available, some will be generated using the given prefix.
mean: mean vector of latent distribution (optional)
cov: covariance matrix of latent distribution (optional)
mcar: proportion of generated data to set to NA (missing completely at random)
grp: a list containing the model and data. See the details section.

Format of a group

A model, or group within a model, is represented as a named list.

spec: list of response model objects
param: numeric matrix of item parameters
free: logical matrix of indicating which parameters are free (TRUE) or fixed (FALSE)
mean: numeric vector giving the mean of the latent distribution
cov: numeric matrix giving the covariance of the latent distribution
data: data.frame containing observed item responses, and optionally, weights and frequencies
score: factors scores with response patterns in rows
weightColumn: name of the data column containing the numeric row weights (optional)
freqColumn: name of the data column containing the integral row frequencies (optional)
qwidth: width of the quadrature expressed in Z units
qpoints: number of quadrature points
minItemsPerScore: minimum number of non-missing items when estimating factor scores

The param matrix stores items parameters by column. If a column has more rows than are required to fully specify a model then the extra rows are ignored. The order of the items in spec and order of columns in param are assumed to match. All items should have the same number of latent dimensions. Loadings on latent dimensions are given in the first few rows and can be named by setting rownames. Item names are assigned by param colnames.

Currently only a multivariate normal distribution is available, parameterized by the mean and cov. If mean and cov are not specified then a standard normal distribution is assumed. The quadrature consists of equally spaced points. For example, qwidth=2 and qpoints=5 would produce points -2, -1, 0, 1, and 2. The quadrature specification is part of the group and not passed as extra arguments for the sake of consistency. As currently implemented, OpenMx uses EAP scores to estimate latent distribution parameters. By default, the exact same EAP scores should be produced by EAPscores.

Examples

Run this code

# 1 dimensional items
i1 <- rpf.drm()
i1.p <- rpf.rparam(i1)
i2 <- rpf.nrm(outcomes=3)
i2.p <- rpf.rparam(i2)
rpf.sample(5, list(i1,i2), list(i1.p, i2.p))