summary-penfa-method: Summary constructor for a 'penfa' object
In penfa: Single- And Multiple-Group Penalized Factor Analysis

Description Usage Arguments Value See Also Examples

An S4 method printing a summary of the model parameter estimates for an object of class penfa.

## S4 method for signature 'penfa'
summary(
  object,
  header = TRUE,
  estimates = TRUE,
  ci = TRUE,
  level = 0.95,
  nd = 3L,
  cutoff = 0.05,
  extra = TRUE
)

`object`	An object of class `penfa`, found as a result of a call to `penfa`.
`header`	Logical. If `TRUE`, the header section is printed. The header contains relevant information about the data, the fitted model, the optimization process, and the penalization strategy, including, for instance, the employed penalties, the estimated effective degrees of freedom (edf), the optimal values of the tuning parameter(s), the GBIC and many others.
`estimates`	Logical. If `TRUE`, a section with the parameter estimates is printed out.
`ci`	Logical. If `TRUE`, confidence intervals are added to the parameter estimates section.
`level`	Logical. It denotes the significance level used for the statistical tests.
`nd`	Integer. It determines the number of digits after the decimal point to be printed in the parameter estimates section.
`cutoff`	Numeric. Standard errors and confidence intervals for the penalized parameter estimates falling below the `cutoff` value are not displayed. Confidence intervals for the parameters that have been penalized and shrunken to zero must be treated with caution.
`extra`	Logical. If `TRUE`, additional information on the model are displayed.

An object reporting a detailed summary of the estimated parameters for a penfa model.

penfa, penfa-class

data(ccdata)

syntax = 'help  =~   h1 + h2 + h3 + h4 + h5 + h6 + h7 + 0*v1 + v2 + v3 + v4 + v5
          voice =~ 0*h1 + h2 + h3 + h4 + h5 + h6 + h7 +   v1 + v2 + v3 + v4 + v5'

alasso_fit <- penfa(## factor model
                    model  = syntax,
                    data   = ccdata,
                    std.lv = TRUE,
                    ## penalization
                    pen.shrink = "alasso",
                    eta = list(shrink = c("lambda" = 0.01), diff = c("none" = 0)),
                    ## automatic procedure
                    strategy = "auto")

summary(alasso_fit)