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).
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 1.000Vignettes and Tutorials
See
vignette("automatic-tuning-selection")for the estimation of a penalized factor model with lasso and alasso penalties. The tuning parameter producing the optimal amount of sparsity in the factor loading matrix is found through the automatic tuning procedure.See
vignette("grid-search-tuning-selection")for the estimation of a penalized factor model with scad and mcp penalties. A grid search is conducted, and the optimal tuning parameter is the one generating the penalized model with the lowest GBIC (Generalized Bayesian Information Criterion).See “multiple-group-analysis” for the estimation of a multiple-group penalized factor model with the alasso penalty. This model encourages sparsity in the loading matrices and cross-group invariance of loadings and intercepts. The automatic multiple tuning parameter procedure is employed for finding the optimal tuning parameter vector.
See “plotting-penalty-matrix” for details on how to produce interactive plots of the penalty matrices.
Literature
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
Geminiani, E. (2020). “A Penalized Likelihood-Based Framework for Single and Multiple-Group Factor Analysis Models.” PhD thesis, University of Bologna. http://amsdottorato.unibo.it/9355/.
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},
#> }
