omitMostMissing: Omit items with the most missing data

Description Usage Arguments Format of a group See Also

View source: R/util.R


Items with no missing data are never omitted, regardless of the number of items requested.


omitMostMissing(grp, omit)



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


the maximum number of items to omit

Format of a group

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


list of response model objects


numeric matrix of item parameters


logical matrix of indicating which parameters are free (TRUE) or fixed (FALSE)


numeric vector giving the mean of the latent distribution


numeric matrix giving the covariance of the latent distribution


data.frame containing observed item responses, and optionally, weights and frequencies


factors scores with response patterns in rows


name of the data column containing the numeric row weights (optional)


name of the data column containing the integral row frequencies (optional)


width of the quadrature expressed in Z units


number of quadrature points


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(), itemOutcomeBySumScore(), observedSumScore(), omitItems(), sumScoreEAP()

rpf documentation built on April 30, 2021, 1:06 a.m.