rpf (version 1.0.14)

itemOutcomeBySumScore: Produce an item outcome by observed sum-score table

Description

Produce an item outcome by observed sum-score table

Usage

itemOutcomeBySumScore(grp, mask, interest)

Arguments

grp

a list containing the model and data. See the details section.

mask

a vector of logicals indicating which items to include

interest

index or name of the item of interest

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.

See Also

Other scoring: EAPscores(), bestToOmit(), observedSumScore(), omitItems(), omitMostMissing(), sumScoreEAP()

Examples

set.seed(1)
spec <- list()
spec[1:3] <- 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)
itemOutcomeBySumScore(grp, c(FALSE,TRUE,TRUE), 1L)