Description Usage Arguments Format of a group See Also

Omit the given items

1 | ```
omitItems(grp, excol)
``` |

`grp` |
a list containing the model and data. See the details section. |

`excol` |
vector of column names to omit |

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.

Other scoring:
`EAPscores()`

,
`bestToOmit()`

,
`itemOutcomeBySumScore()`

,
`observedSumScore()`

,
`omitMostMissing()`

,
`sumScoreEAP()`

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