Return the model coefficients of a ppLasso or gr_ppLasso object
Arguments
- fit
a
ppLassoorgr_ppLassoobject.- lambda
values of the regularization parameter lambda at which coefficients are requested. For values of lambda not in the sequence of fitted models, linear interpolation is used.
- which
indices of the penalty parameter lambda at which predictions are required. By default, all indices are returned. If lambda is specified, this will override which.
- drop
whether to keep coefficient names
- ...
Examples
#fit glm without grouped covariates
data(BinaryData)
data <- BinaryData$data
Y.char <- BinaryData$Y.char
prov.char <- BinaryData$prov.char
Z.char <- BinaryData$Z.char
fit <- pp.lasso(data, Y.char, Z.char, prov.char)
coef(fit, lambda = fit$lambda)$beta[, 1:10]
#> 0.132 0.1202 0.1096 0.0998 0.091 0.0829 0.0755 0.0688
#> Z1 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> Z2 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> Z3 0 0.1120347 0.2148847 0.3098777 0.3980465 0.4801436 0.5568409 0.6285796
#> Z4 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> Z5 0 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 0.0627 0.0571
#> Z1 0.0000000 0.0000000
#> Z2 0.0000000 0.0000000
#> Z3 0.6957436 0.7586491
#> Z4 0.0000000 0.0000000
#> Z5 0.0000000 0.0000000
coef(fit, lambda = fit$lambda)$gamma[1:10, 1:5]
#> 0.132 0.1202 0.1096 0.0998 0.091
#> 1 -0.2411977 -0.2939898 -0.3431091 -0.3889330 -0.4318211
#> 2 -1.9635362 -1.8742433 -1.7984115 -1.7336457 -1.6779754
#> 3 -1.2089403 -1.1883078 -1.1712888 -1.1572109 -1.1455229
#> 4 -1.9600386 -1.8922127 -1.8332183 -1.7815619 -1.7360041
#> 5 -0.5500569 -0.5698456 -0.5894538 -0.6087582 -0.6276779
#> 6 -1.3694159 -1.2813234 -1.2034985 -1.1342974 -1.0723356
#> 7 -0.4661063 -0.4936788 -0.5199327 -0.5449261 -0.5687380
#> 8 -0.4274344 -0.4203383 -0.4151684 -0.4115749 -0.4092523
#> 9 1.0329137 0.8945008 0.7694552 0.6557870 0.5517900
#> 10 -0.9075577 -0.9146380 -0.9232143 -0.9328743 -0.9433025
#fit glm with grouped covariates
data(BinaryData)
data <- BinaryData$data
Y.char <- BinaryData$Y.char
prov.char <- BinaryData$prov.char
Z.char <- BinaryData$Z.char
group <- BinaryData$group
fit <- grp.lasso(data, Y.char, Z.char, prov.char, group = group)
coef(fit, lambda = fit$lambda)$beta[, 1:5]
#> 0.0939 0.0856 0.078 0.071 0.0647
#> Z1 0 0.00000000 0.00000000 -0.003334178 -0.05627111
#> Z2 0 0.00000000 0.00000000 0.003875496 0.06351446
#> Z3 0 0.11268128 0.21468084 0.307301593 0.38977908
#> Z4 0 -0.01162498 -0.01401619 -0.009610844 -0.00190843
#> Z5 0 0.00000000 0.00000000 0.000000000 0.00000000
coef(fit, lambda = fit$lambda)$gamma[1:10, 1:5]
#> 0.0939 0.0856 0.078 0.071 0.0647
#> 1 -0.2411731 -0.2890605 -0.3366646 -0.3843345 -0.4426426
#> 2 -1.9635866 -1.8817822 -1.8080178 -1.7414115 -1.6832827
#> 3 -1.2089543 -1.1907172 -1.1742371 -1.1589555 -1.1394428
#> 4 -1.9600775 -1.8990257 -1.8418545 -1.7882081 -1.7355165
#> 5 -0.5500497 -0.5681728 -0.5871905 -0.6066594 -0.6253165
#> 6 -1.3694650 -1.2892476 -1.2136979 -1.1425348 -1.0753747
#> 7 -0.4660951 -0.4915285 -0.5170385 -0.5424109 -0.5680985
#> 8 -0.4274407 -0.4200726 -0.4148432 -0.4114738 -0.4111511
#> 9 1.0329839 0.9085190 0.7875110 0.6698040 0.5477673
#> 10 -0.9075578 -0.9153895 -0.9239389 -0.9332905 -0.9470281