Get the support for individual models
get.individual.support.RdGet the indices of nonzero coefficients from the individual models in a fitted ptLasso or cv.ptLasso object, excluding the intercept.
Usage
get.individual.support(
fit,
s = "lambda.min",
gamma = "gamma.min",
commonOnly = FALSE,
groups = 1:length(fit$fitind)
)Arguments
- fit
fitted
"ptLasso"or"cv.ptLasso"object.- s
the choice of lambda to use. May be "lambda.min", "lambda.1se" or a numeric value. Default is "lambda.min".
- gamma
for use only when 'relax = TRUE' was specified during training. The choice of 'gamma' to use. May be "gamma.min" or "gamma.1se". Default is "gamma.min".
- commonOnly
whether to return the features that are chosen by more than half of the group- or response-specific models (TRUE) or the features that are chosen by any of the group-specific models (FALSE). Default is FALSE.
- groups
which groups or responses to include when computing the support. Default is to include all groups/responses.
Value
This returns a vector containing the indices of nonzero coefficients (excluding the intercept).
Examples
# Train data
set.seed(1234)
out = gaussian.example.data()
x = out$x; y=out$y; groups = out$group;
fit = ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.individual.support(fit)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 25 27 28 29 30 31 32 34 35 36 37 38 39
#> [37] 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
#> [55] 58 59 60 62 63 64 65 66 67 68 69 70 72 73 75 76 77 78
#> [73] 79 80 82 83 84 87 88 89 90 91 92 93 94 95 98 99 100 101
#> [91] 102 103 104 105 106 107 108 109 110 111 112 113 114 115 117 118 119 120
# only return features common to all groups
get.individual.support(fit, commonOnly = TRUE)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 37 38 48 51 54 60
#> [20] 65 67 73 80 82 83 108 118
# group 1 only
get.individual.support(fit, groups = 1)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 22 29
#> [20] 31 32 34 37 38 47 48 50 54 57 58 59 60 62 64 66 67 70 72
#> [39] 73 77 79 80 83 88 89 91 93 94 95 101 103 104 106 108 111 113 114
#> [58] 115 117 118 119
cvfit = cv.ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.individual.support(cvfit)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#> [19] 19 20 21 22 23 25 27 28 29 30 31 32 34 35 36 37 38 39
#> [37] 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
#> [55] 58 59 60 61 62 63 64 65 66 67 68 69 70 72 73 75 76 77
#> [73] 78 79 80 82 83 84 87 88 89 90 91 92 93 94 95 98 99 100
#> [91] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 117 118 119
#> [109] 120
# group 1 only
get.individual.support(cvfit, groups = 1)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 19 20
#> [20] 22 29 31 32 34 37 38 43 47 48 50 54 56 57 58 59 60 62 64
#> [39] 65 66 67 70 72 73 75 77 79 80 83 88 89 90 91 93 94 95 101
#> [58] 103 104 106 108 111 113 114 115 117 118 119