This is a simple convenience function designed to facilitate
looking at specific parameter types by easily return a subset
of a data frame with those types only. It is designed to follow up
the results returned from the `readModels`

function.

paramExtract(
x,
params = c("regression", "loading", "undirected", "expectation", "variability",
"new")
)

## Arguments

x |
A data frame (specifically the type returned by `readModels` ) containing
parameters. Should be specific such as unstandardized and the data frame must have a
column called ‘paramHeader’. |

params |
A character string indicating the types of parameters to be returned.
Options currently include ‘regression’, ‘loading’, ‘undirected’,
‘expectation’, ‘variability’, and ‘new’ for new/additional parameters.
Regressions include regression of one variable
`ON` another. ‘loading’ include indicator variables (which are assumed caused by the
underlying latent variable) and variables in latent growth models (`BY` or `|` ).
Undirected paths currently only include covariances, indicated by the `WITH`
syntax in Mplus. Expectation paths are the unconditional or conditional expectations of
variables. In other words those parameters related to the first moments. For independent
variables, these are the means, \(E(X)\) and the conditional means or intercepts,
\(E(X | f(\theta))\) where \(f(\theta)\) is the model,
some function of the parameters, \(\theta\). Finally ‘variability’
refers to both variances and residual variances, corresponding to the second moments.
As with the expectations, variances are unconditional for variables that are not
predicted or conditioned on any other variable in the model whereas residual variances
are conditional on the model. Note that R uses fuzzy matching so that each of these
can be called via shorthand, ‘r’, ‘l’, ‘u’, ‘e’, and ‘v’. |

## Value

A subset data frame with the parameters of interest.

## See also

## Author

Joshua F. Wiley <jwiley.psych@gmail.com>

## Examples