Get the indices of nonzero coefficients from the pretrained models in a fitted ptLasso or cv.ptLasso object, excluding the intercept.
Usage
get.pretrain.support(
fit,
s = "lambda.min",
gamma = "gamma.min",
commonOnly = FALSE,
includeOverall = TRUE,
groups = 1:length(fit$fitind)
)Arguments
- fit
fitted
"ptLasso"or"cv.ptLasso"object.- s
the choice of lambda to use. May be "lambda.min", "lambda.1se" or a numeric value. Default is "lambda.min".
- gamma
for use only when 'relax = TRUE' was specified during training. The choice of 'gamma' to use. May be "gamma.min" or "gamma.1se". Default is "gamma.min".
- commonOnly
whether to return the features that are chosen by more than half of the group- or response-specific models (TRUE) or the features that are chosen by any of the group-specific models (FALSE). Default is FALSE.
- includeOverall
whether to return the features that are chosen by the overall model and not the group-specific models (TRUE) or the features that are chosen by the overall model or the group-specific models (FALSE). Default is TRUE. Not used when 'use.case = "timeSeries"'.
- groups
which groups or responses to include when computing the support. Default is to include all groups/responses.
Value
If a ptLasso object is supplied, this returns a vector containing the indices of nonzero coefficients (excluding the intercept). If a cv.ptLasso object is supplied, this returns a list of results - one for each value of alpha.
Examples
# Train data
set.seed(1234)
out = gaussian.example.data(k=2, class.sizes = c(50, 50))
x = out$x; y=out$y; groups = out$group;
fit = ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.pretrain.support(fit)
#> [1] 1 4 5 6 7 11 40 45 46 58
# only return features common to all groups
get.pretrain.support(fit, commonOnly = TRUE)
#> [1] 4 5 7
# group 1 only, don't include the overall model support
get.pretrain.support(fit, groups = 1, includeOverall = FALSE)
#> integer(0)
# group 1 only, include the overall model support
get.pretrain.support(fit, groups = 1, includeOverall = TRUE)
#> [1] 4 5 7
cvfit = cv.ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.pretrain.support(cvfit)
#> [[1]]
#> [1] 4 5 7
#>
#> [[2]]
#> [1] 4 5 6 7 11 45 58
#>
#> [[3]]
#> [1] 1 2 4 5 6 7 8 11 26 40 45 46 58
#>
#> [[4]]
#> [1] 1 2 4 5 6 7 11 26 40 45 46 58
#>
#> [[5]]
#> [1] 1 4 5 6 7 11 40 45 46 58
#>
#> [[6]]
#> [1] 1 4 5 6 7 11 40 45 46 58
#>
#> [[7]]
#> [1] 1 2 4 5 6 7 8 11 26 40 45 46 58
#>
#> [[8]]
#> [1] 1 2 4 5 6 7 8 11 26 40 45 46 58
#>
#> [[9]]
#> [1] 1 2 4 5 6 7 8 11 26 40 45 46 58
#>
#> [[10]]
#> [1] 1 2 4 5 6 7 8 9 10 11 13 22 25 26 28 31 32 34 40 42 45 46 51 58
#>
#> [[11]]
#> [1] 1 2 4 5 6 7 8 9 10 11 12 13 22 25 26 28 31 32 34 40 41 42 45 46 51
#> [26] 54 58
#>
get.pretrain.support(cvfit, groups = 1)
#> [[1]]
#> [1] 4 5 7
#>
#> [[2]]
#> [1] 4 5 7
#>
#> [[3]]
#> [1] 4 5 7
#>
#> [[4]]
#> [1] 4 5 7
#>
#> [[5]]
#> [1] 4 5 7
#>
#> [[6]]
#> [1] 4 5 7
#>
#> [[7]]
#> [1] 4 5 7
#>
#> [[8]]
#> [1] 4 5 7
#>
#> [[9]]
#> [1] 4 5 7
#>
#> [[10]]
#> [1] 4 5 7
#>
#> [[11]]
#> [1] 6 32 54
#>