EAPscores: Compute Expected A Posteriori (EAP) scores

Description

If you have missing data then you must specify minItemsPerScore. This option will set scores to NA when there are too few items to make an accurate score estimate. If you are using the scores as point estimates without considering the standard error then you should set minItemsPerScore as high as you can tolerate. This will increase the amount of missing data but scores will be more accurate. If you are carefully considering the standard errors of the scores then you can set minItemsPerScore to 1. This will mimic the behavior of most other IFA software wherein scores are estimated if there is at least 1 non-NA item for the score. However, it may make more sense to set minItemsPerScore to 0. When set to 0, all NA rows are scored to the prior distribution.

Usage

EAPscores(grp, ..., compressed = FALSE)

Arguments

grp: a list containing the model and data. See the details section.
...: Not used. Forces remaining arguments to be specified by name.
compressed: output one score per observed data row even when freqColumn is set (default FALSE)

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.

Details

Output is not affected by the presence of a weightColumn.

Examples

Run this code

spec <- list()
spec[1:3] <- list(rpf.grm(outcomes=3))
param <- sapply(spec, rpf.rparam)
data <- rpf.sample(5, spec, param)
colnames(param) <- colnames(data)
grp <- list(spec=spec, param=param, data=data, minItemsPerScore=1L)
EAPscores(grp)