If a reference column is given then only rows that are not missing on the reference column are considered. Otherwise all rows are considered.
bestToOmit(grp, omit, ref = NULL)a list containing the model and data. See the details section.
the maximum number of items to omit
the reference column (optional)
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.
Other scoring:
EAPscores(),
itemOutcomeBySumScore(),
observedSumScore(),
omitItems(),
omitMostMissing(),
sumScoreEAP()