Summary Statistics for a SEM Analysis

This is a low level fitting function, for SEMSummary.

Usage

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

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”.

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.