An S4 method printing a summary of the model parameter estimates for an
object of class penfa.
# S4 method for penfa summary( object, header = TRUE, estimates = TRUE, ci = TRUE, level = 0.95, nd = 3L, cutoff = 0.05, extra = TRUE )
| object | An object of class |
|---|---|
| header | Logical. If |
| estimates | Logical. If |
| ci | Logical. If |
| 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 |
| extra | Logical. If |
An object reporting a detailed summary of the estimated parameters
for a penfa model.
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")#> 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#> 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 #>