The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

penfa

minimal R version lifecycle Last-changedate Website Licence R-CMD-check

Overview

An R package for estimating single- and multiple-group penalized factor models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021). Supported penalties include lasso, adaptive lasso, scad, mcp, and ridge.

Installation

You can install the released version of penfa from CRAN with:

install.packages("penfa")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("egeminiani/penfa")

Example

This is a basic example showing how to fit a PENalized Factor Analysis model with the alasso penalty and the automatic tuning procedure. A shrinkage penalty is applied to the whole factor loading matrix.

Let’s load the data (see ?ccdata for details).

library(penfa)
data(ccdata)

Step 1 : specify the model syntax

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'

Step 2: fit the model

alasso_fit <- penfa(model  = syntax,
                    data   = ccdata,
                    std.lv = TRUE,
                    pen.shrink = "alasso")
#> 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
alasso_fit
#> penfa 0.1.1 reached convergence
#> 
#>   Number of observations                                    767
#>                                                                
#>   Estimator                                                PMLE
#>   Optimization method                              trust-region
#>   Information                                            fisher
#>   Strategy                                                 auto
#>   Number of iterations (total)                               58
#>   Number of two-steps (automatic)                             2
#>   Effective degrees of freedom                           27.129
#>                                                                
#>   Penalty function:                                            
#>     Sparsity                                             alasso
#>                                                                
#> 

Step 3: inspect the results

summary(alasso_fit)
#> penfa 0.1.1 reached convergence
#> 
#>   Number of observations                                    767
#>   Number of groups                                            1
#>   Number of observed variables                               12
#>   Number of latent factors                                    2
#>                                                                
#>   Estimator                                                PMLE
#>   Optimization method                              trust-region
#>   Information                                            fisher
#>   Strategy                                                 auto
#>   Number of iterations (total)                               58
#>   Number of two-steps (automatic)                             2
#>   Influence factor                                            4
#>   Number of parameters:                                        
#>     Free                                                     13
#>     Penalized                                                22
#>   Effective degrees of freedom                           27.129
#>   GIC                                                 17222.980
#>   GBIC                                                17348.928
#>                                                                
#>   Penalty function:                                            
#>     Sparsity                                             alasso
#>                                                                
#>   Additional tuning parameter                                  
#>     alasso                                                    1
#>                                                                
#>   Optimal tuning parameter:                                    
#>     Sparsity                                                   
#>      - Factor loadings                                    0.005
#>                                                                
#> 
#> Parameter Estimates:
#> 
#> Latent Variables:
#>                     Type    Estimate  Std.Err     2.5%    97.5%
#>   help =~                                                      
#>     h1               pen       0.766    0.030    0.707    0.825
#>     h2               pen       0.858    0.028    0.803    0.913
#>     h3               pen       0.775    0.030    0.717    0.834
#>     h4               pen       0.921    0.038    0.847    0.995
#>     h5               pen       0.810    0.040    0.732    0.887
#>     h6               pen       0.782    0.044    0.696    0.868
#>     h7               pen       0.523    0.050    0.426    0.620
#>     v1             fixed       0.000             0.000    0.000
#>     v2               pen       0.000                           
#>     v3               pen       0.000                           
#>     v4               pen       0.000                           
#>     v5               pen      -0.000                           
#>   voice =~                                                     
#>     h1             fixed       0.000             0.000    0.000
#>     h2               pen      -0.000                           
#>     h3               pen       0.000                           
#>     h4               pen      -0.041                           
#>     h5               pen       0.053    0.031   -0.008    0.114
#>     h6               pen       0.104    0.038    0.029    0.180
#>     h7               pen       0.341    0.049    0.246    0.437
#>     v1               pen       0.851    0.028    0.795    0.906
#>     v2               pen       0.871    0.028    0.817    0.926
#>     v3               pen       0.842    0.029    0.786    0.898
#>     v4               pen       0.843    0.029    0.787    0.899
#>     v5               pen       0.805    0.029    0.747    0.862
#> 
#> Covariances:
#>                     Type    Estimate  Std.Err     2.5%    97.5%
#>   help ~~                                                      
#>     voice           free       0.877    0.011    0.855    0.900
#> 
#> Variances:
#>                     Type    Estimate  Std.Err     2.5%    97.5%
#>    .h1              free       0.388    0.021    0.346    0.429
#>    .h2              free       0.233    0.014    0.205    0.261
#>    .h3              free       0.372    0.021    0.332    0.413
#>    .h4              free       0.184    0.012    0.160    0.209
#>    .h5              free       0.235    0.014    0.207    0.263
#>    .h6              free       0.201    0.012    0.177    0.225
#>    .h7              free       0.264    0.015    0.235    0.293
#>    .v1              free       0.245    0.015    0.216    0.275
#>    .v2              free       0.208    0.014    0.182    0.235
#>    .v3              free       0.261    0.016    0.230    0.292
#>    .v4              free       0.259    0.016    0.228    0.290
#>    .v5              free       0.324    0.019    0.287    0.361
#>     help           fixed       1.000             1.000    1.000
#>     voice          fixed       1.000             1.000    1.000

Vignettes and Tutorials

Literature

How to cite

#> 
#> To cite penfa in publications use:
#> 
#>   Geminiani, E., Marra, G., & Moustaki, I. (2021). Single- and
#>   Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm
#>   Approach with Integrated Automatic Multiple Tuning Parameter
#>   Selection.  Psychometrika, 86(1), 65-95.
#>   https://doi.org/10.1007/s11336-021-09751-8
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {Single- and Multiple-Group Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated Automatic Multiple Tuning Parameter Selection},
#>     author = {Geminiani Elena and Marra Giampiero and Moustaki Irini},
#>     journal = {Psychometrika},
#>     year = {2021},
#>     volume = {86},
#>     number = {1},
#>     pages = {65-95},
#>     url = {https://doi.org/10.1007/s11336-021-09751-8},
#>   }
#> 
#>   Elena Geminiani, Giampiero Marra and Irini Moustaki (2021). penfa:
#>   Single- And Multiple-Group Penalized Factor Analysis. R package
#>   version 0.1.1.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {penfa: Single- And Multiple-Group Penalized Factor Analysis},
#>     author = {Elena Geminiani and Giampiero Marra and Irini Moustaki},
#>     year = {2021},
#>     note = {R package version 0.1.1},
#>   }

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.