summary,penfa-method {penfa}

Summary constructor for a penfa object

Description

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

Usage

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

Arguments

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.

Value

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

See Also

penfa, penfa-class

Examples

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)