Compute the factor scores from a fitted penfa model

The penfaPredict function estimates the factor scores from a fitted penalized factor model. The factor scores are the estimated values ("predictions") of the common factors.

penfaPredict(
  object,
  newdata = NULL,
  method = "regression",
  label = TRUE,
  append.data = FALSE,
  assemble = FALSE
)

Arguments

object	An object of class penfa.
newdata	An optional data frame containing the same variables as the ones appearing in the original data frame used for fitting the model in object.
method	Character indicating the method for computing the factor scores. Possible options are "regression" and "bartlett". For the normal linear continuous case, the regression method is equivalent to the Empirical Bayes Method (EBM), whereas Bartlett's strategy is equivalent to maximum likelihood's method.
label	Logical. If TRUE, the columns are labeled.
append.data	Logical. If TRUE, the original data set (or the data set provided in the newdata argument) is appended to the factor scores.
assemble	Logical. If TRUE, the factor scores from each group are assembled in a single data frame of the same dimensions as the original data set and with a group column defining the groups.

Value

A matrix with the factor scores from a fitted penfa model.

References

Geminiani E. (2020), "A penalized likelihood-based framework for single and multiple-group factor analysis models" (Doctoral dissertation, University of Bologna). Available at http://amsdottorato.unibo.it/9355/.

See also

penfa

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",
                    gamma = 4)
#> Computing weights for alasso (ML estimates)... done.
#> 
#> Automatic procedure: 
#> Iteration  1 : 0.00298271 
#> Iteration  2 : 0.00452604 
#> 
#> Largest absolute gradient value: 12.76355181
#> Fisher information matrix is positive definite
#> Eigenvalue range: [180.2917, 9189645]
#> Trust region iterations: 15 
#> Factor solution: admissible 
#> Effective degrees of freedom: 27.12936  

fscores <- penfaPredict(alasso_fit)