sumScoreEAP function

Compute the sum-score EAP table

Compute the sum-score EAP table

Observed tables cannot be computed when data is missing. Therefore, you can optionally omit items with the greatest number of responses missing when conducting the distribution test.

sumScoreEAP(grp, ..., qwidth = 6, qpoints = 49L, .twotier = TRUE)

Arguments

  • grp: a list containing the model and data. See the details section.
  • ...: Not used. Forces remaining arguments to be specified by name.
  • qwidth: DEPRECATED
  • qpoints: DEPRECATED
  • .twotier: whether to enable the two-tier optimization

Details

When two-tier covariance structure is detected, EAP scores are only reported for primary factors. It is possible to compute EAP scores for specific factors, but it is not clear why this would be useful because they are conditional on the specific factor sum scores. Moveover, the algorithm to compute them efficiently has not been published yet (as of Jun 2014).

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

# see Thissen, Pommerich, Billeaud, & Williams (1995, Table 2)
 spec <- list()
 spec[1:3] <- list(rpf.grm(outcomes=4))

 param <- matrix(c(1.87, .65, 1.97, 3.14,
                   2.66, .12, 1.57, 2.69,
                   1.24, .08, 2.03, 4.3), nrow=4)
 # fix parameterization
 param <- apply(param, 2, function(p) c(p[1], p[2:4] * -p[1]))

 grp <- list(spec=spec, mean=0, cov=matrix(1,1,1), param=param)
 sumScoreEAP(grp)

See Also

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

rpf packageRead PDF manual