lifecycle::badge("experimental") This was last updated in 2017 and may no longer work.
write.flexmirt(groups, file = NULL, fileEncoding = "")a list of groups each with items and latent parameters
the destination file name
how to encode the text file (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.
Formats item parameters in the way that flexMIRT expects to read them.
NOTE: Support for the graded response model may not be complete.