Print parameter estimates in table format

The parameter estimates of the penalized factor analysis model in each group.

penfaParEstim(
  object,
  se = TRUE,
  ci = TRUE,
  level = 0.95,
  remove.nonfree = FALSE,
  output = "data.frame",
  header = FALSE
)

Arguments

object	An object of class `penfa`.
se	Logical. If `TRUE`, it includes a column with the standard errors.
ci	Logical. If `TRUE`, the confidence intervals are added to the output.
level	The confidence level, default is 0.95.
remove.nonfree	Logical. If `TRUE`, it filters the output and removes all rows with fixed (that is, neither free, nor penalized) parameters.
output	Character. If "data.frame", the parameter table is displayed as a standard formatted data.frame. If "text", the parameter table is displayed with subsections (as used by the `summary` function).
header	Logical, only used if `output = "text"`. If `TRUE`, it prints a header on top of the parameter list with details on the group levels and the information matrix used during optimization by the trust-region algorithm.

Value

A dataframe of class penfa.data.frame with the parameter estimates of a penfa model for each group.

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")
#> 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  

penfaParEstim(alasso_fit)
#>      lhs op   rhs  type penalty           est           se      ci_0.025
#> 1   help =~    h1   pen  shrink  7.658121e-01 0.0300355828  0.7069434576
#> 2   help =~    h2   pen  shrink  8.579551e-01 0.0281200574  0.8028407715
#> 3   help =~    h3   pen  shrink  7.753391e-01 0.0298554161  0.7168235129
#> 4   help =~    h4   pen  shrink  9.208458e-01 0.0378462170  0.8466685372
#> 5   help =~    h5   pen  shrink  8.096618e-01 0.0395996245  0.7320479239
#> 6   help =~    h6   pen  shrink  7.820608e-01 0.0438313584  0.6961528687
#> 7   help =~    h7   pen  shrink  5.230518e-01 0.0495779689  0.4258807765
#> 8   help =~    v1 fixed    none  0.000000e+00 0.0000000000  0.0000000000
#> 9   help =~    v2   pen  shrink  4.013798e-05 0.0013109918 -0.0025293588
#> 10  help =~    v3   pen  shrink  2.389271e-05 0.0012510188 -0.0024280590
#> 11  help =~    v4   pen  shrink  8.563583e-07 0.0006059277 -0.0011867401
#> 12  help =~    v5   pen  shrink -9.202527e-07 0.0003299068 -0.0006475258
#> 13 voice =~    h1 fixed    none  0.000000e+00 0.0000000000  0.0000000000
#> 14 voice =~    h2   pen  shrink -1.287100e-05 0.0009545266 -0.0018837087
#> 15 voice =~    h3   pen  shrink  5.304850e-06 0.0006379165 -0.0012449884
#> 16 voice =~    h4   pen  shrink -4.119937e-02 0.0279540523 -0.0959883072
#> 17 voice =~    h5   pen  shrink  5.262033e-02 0.0311089159 -0.0083520210
#> 18 voice =~    h6   pen  shrink  1.042524e-01 0.0384810259  0.0288309311
#> 19 voice =~    h7   pen  shrink  3.412021e-01 0.0487496049  0.2456545891
#> 20 voice =~    v1   pen  shrink  8.507219e-01 0.0283398664  0.7951768003
#> 21 voice =~    v2   pen  shrink  8.714683e-01 0.0278826758  0.8168192712
#> 22 voice =~    v3   pen  shrink  8.418956e-01 0.0285565995  0.7859257263
#> 23 voice =~    v4   pen  shrink  8.429135e-01 0.0285202472  0.7870148371
#> 24 voice =~    v5   pen  shrink  8.047512e-01 0.0293422229  0.7472414679
#> 25    h1 ~~    h1  free    none  3.875064e-01 0.0213756233  0.3456109291
#> 26    h2 ~~    h2  free    none  2.331226e-01 0.0141372069  0.2054141415
#> 27    h3 ~~    h3  free    none  3.723067e-01 0.0206500165  0.3318334075
#> 28    h4 ~~    h4  free    none  1.844461e-01 0.0124028238  0.1601370438
#> 29    h5 ~~    h5  free    none  2.349729e-01 0.0140838417  0.2073691125
#> 30    h6 ~~    h6  free    none  2.009585e-01 0.0123597415  0.1767338671
#> 31    h7 ~~    h7  free    none  2.638821e-01 0.0147092960  0.2350523666
#> 32    v1 ~~    v1  free    none  2.454065e-01 0.0151161227  0.2157794305
#> 33    v2 ~~    v2  free    none  2.083625e-01 0.0135010799  0.1819008483
#> 34    v3 ~~    v3  free    none  2.607043e-01 0.0158005083  0.2297358369
#> 35    v4 ~~    v4  free    none  2.590440e-01 0.0157260085  0.2282215497
#> 36    v5 ~~    v5  free    none  3.240195e-01 0.0187007932  0.2873666333
#> 37  help ~~  help fixed    none  1.000000e+00 0.0000000000  1.0000000000
#> 38 voice ~~ voice fixed    none  1.000000e+00 0.0000000000  1.0000000000
#> 39  help ~~ voice  free    none  8.771535e-01 0.0114966949  0.8546204111
#>        ci_0.975
#> 1  0.8246807786
#> 2  0.9130693711
#> 3  0.8338545936
#> 4  0.9950229819
#> 5  0.8872755994
#> 6  0.8679686366
#> 7  0.6202228433
#> 8  0.0000000000
#> 9  0.0026096347
#> 10 0.0024758444
#> 11 0.0011884528
#> 12 0.0006456853
#> 13 0.0000000000
#> 14 0.0018579667
#> 15 0.0012555981
#> 16 0.0135895642
#> 17 0.1135926887
#> 18 0.1796737806
#> 19 0.4367495288
#> 20 0.9062670353
#> 21 0.9261173518
#> 22 0.8978655393
#> 23 0.8988121516
#> 24 0.8622608681
#> 25 0.4294018327
#> 26 0.2608309742
#> 27 0.4127799846
#> 28 0.2087552197
#> 29 0.2625767574
#> 30 0.2251831634
#> 31 0.2927117476
#> 32 0.2750335428
#> 33 0.2348241091
#> 34 0.2916726914
#> 35 0.2898663701
#> 36 0.3606723956
#> 37 1.0000000000
#> 38 1.0000000000
#> 39 0.8996866268

Print parameter estimates in table format

Arguments

Value

See also

Examples