This is a low level fitting function, for SEMSummary.

## Usage

```
SEMSummary.fit(
formula,
data,
use = c("fiml", "pairwise.complete.obs", "complete.obs")
)
```

## Arguments

- formula
A formula of the variables to be used in the analysis. See the ‘details’ section for more information.

- data
A data frame, matrix, or list containing the variables used in the formula. This is a required argument.

- use
A character vector of how to handle missing data. Defaults to “fiml”.

## Value

A list with S3 class “SEMSummary”

- names
A character vector containing the variable names.

- n
An integer vector of the length of each variable used (this includes available and missing data).

- nmissing
An integer vector of the number of missing values in each variable.

- mu
A vector of the arithmetic means of each variable (on complete data).

- stdev
A numeric vector of the standard deviations of each variable (on complete data).

- Sigma
The numeric covariance matrix for all variables.

- sSigma
The numeric correlation matrix for all variables.

- coverage
A numeric matrix giving the percentage (technically decimal) of information available for each pairwise covariance/correlation.

- pvalue
The two-sided p values for the correlation matrix. Pairwise present N used to calculate degrees of freedom.