Get the support for pretrained models
get.pretrain.support.RdGet 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$fitind)
)Arguments
- fit
fitted
"ptLasso"or"cv.ptLasso"object.- s
the choice of lambda to use. May be "lambda.min", "lambda.1se" or a numeric value. Default is "lambda.min".
- gamma
for use only when 'relax = TRUE' was specified during training. The choice of 'gamma' to use. May be "gamma.min" or "gamma.1se". Default is "gamma.min".
- commonOnly
whether to return the features that are chosen by more than half of the group- or response-specific models (TRUE) or the features that are chosen by any of the group-specific models (FALSE). Default is FALSE.
- includeOverall
whether to return the features that are chosen by the overall model and not the group-specific models (TRUE) or the features that are chosen by the overall model or the group-specific models (FALSE). Default is TRUE. Not used when 'use.case = "timeSeries"'.
- groups
which groups or responses to include when computing the support. Default is to include all groups/responses.
Value
If a ptLasso object is supplied, this returns a vector containing the indices of nonzero coefficients (excluding the intercept). If a cv.ptLasso object is supplied, this returns a list of results - one for each value of alpha.
Examples
# Train data
set.seed(1234)
out = gaussian.example.data()
x = out$x; y=out$y; groups = out$group;
fit = ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.pretrain.support(fit)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 19 20 21
#> [20] 22 23 25 27 28 29 30 31 32 34 35 37 38 39 41 42 43 44 45
#> [39] 46 47 48 49 50 51 52 53 54 55 56 57 59 60 62 64 65 66 67
#> [58] 68 69 72 73 75 76 77 79 80 82 83 84 88 89 90 94 97 98 99
#> [77] 100 102 105 108 109 110 111 112 114 115 118 120
# only return features common to all groups
get.pretrain.support(fit, commonOnly = TRUE)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 51 54 56 57 65 69 80 83 84 88
# group 1 only, don't include the overall model support
get.pretrain.support(fit, groups = 1, includeOverall = FALSE)
#> [1] 1 5 11 13 14 15 16 20 31 32 34 57 64 72 73 80 83 88 89
#> [20] 94 111 114
# group 1 only, include the overall model support
get.pretrain.support(fit, groups = 1, includeOverall = TRUE)
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 20 31 32 34
#> [20] 54 56 57 64 69 72 73 80 83 84 88 89 94 111 114
cvfit = cv.ptLasso(x, y, groups = groups, family = "gaussian", type.measure = "mse")
get.pretrain.support(cvfit)
#> [[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 27 35 36 38 45 50 52 54
#> [20] 56 57 61 62 63 69 80 84 88 100
#>
#> [[2]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20 22
#> [20] 24 25 27 29 31 32 34 35 36 37 38 43 45 47 48 50 52 53 54
#> [39] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 72 73 75
#> [58] 80 83 84 88 89 90 91 93 94 100 103 106 108 111 113 114 115 117 118
#> [77] 119
#>
#> [[3]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 21 22
#> [20] 23 25 27 29 30 31 32 34 35 36 37 38 43 45 46 47 48 50 51
#> [39] 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 69 70 72
#> [58] 73 75 80 82 83 84 88 89 91 94 100 103 106 108 110 111 113 114 115
#> [77] 117 119 120
#>
#> [[4]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 21 22
#> [20] 23 27 29 30 31 32 34 35 36 37 38 41 42 43 45 46 47 48 49
#> [39] 50 51 52 53 54 55 56 57 60 61 62 63 64 65 66 67 69 72 73
#> [58] 75 79 80 83 84 88 89 90 91 94 100 106 108 109 110 111 114 115 117
#> [77] 119 120
#>
#> [[5]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 20 21 22 23 25 27
#> [20] 29 30 32 35 36 37 38 41 42 43 45 46 47 48 49 50 51 52 53
#> [39] 54 55 56 57 59 60 61 62 63 65 69 75 79 80 82 83 84 88 90
#> [58] 100 105 108 110 120
#>
#> [[6]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 20 21 22 23
#> [20] 25 27 29 30 31 32 34 35 36 37 38 41 42 43 45 46 47 48 49
#> [39] 50 51 52 53 54 55 56 57 59 60 61 62 63 64 65 66 69 72 73
#> [58] 75 76 77 79 80 83 84 88 89 90 94 99 100 108 109 110 111 112 114
#> [77] 117 118 120
#>
#> [[7]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 21 22
#> [20] 23 25 27 28 29 30 31 32 34 35 36 37 38 39 40 41 42 43 44
#> [39] 45 46 47 48 49 50 51 52 53 54 55 56 57 59 60 61 62 63 64
#> [58] 65 66 67 68 69 72 73 75 76 77 79 80 82 83 84 88 89 90 91
#> [77] 93 94 98 99 100 102 105 108 109 110 111 112 113 114 115 117 118 119 120
#>
#> [[8]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 19 20
#> [20] 21 22 23 25 27 28 29 30 31 32 34 35 36 37 38 39 40 41 42
#> [39] 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 59 60 61 62
#> [58] 63 64 65 66 67 68 69 72 73 75 76 77 79 80 82 83 84 88 89
#> [77] 90 93 94 98 99 100 102 105 108 109 110 111 112 113 114 115 118 120
#>
#> [[9]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
#> [20] 20 21 22 23 25 27 28 29 30 32 35 36 37 38 39 40 41 42 43
#> [39] 44 45 46 47 48 49 50 51 52 53 54 55 56 57 59 60 61 62 63
#> [58] 64 65 66 67 68 69 72 73 75 76 77 79 80 82 83 84 88 89 90
#> [77] 93 94 98 99 100 102 105 108 109 110 111 112 115 118 120
#>
#> [[10]]
#> [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 97 98 99
#> [91] 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 117 118
#> [109] 119 120
#>
#> [[11]]
#> [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
#>
get.pretrain.support(cvfit, groups = 1)
#> [[1]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 27 35 36 38 45 50 52 54
#> [20] 56 57 61 62 63 69 80 84 88 100
#>
#> [[2]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20 22
#> [20] 24 25 27 29 31 32 34 35 36 37 38 43 45 47 48 50 52 54 56
#> [39] 57 58 59 60 61 62 63 64 65 66 67 68 69 70 72 73 75 80 83
#> [58] 84 88 89 90 91 93 94 100 103 106 108 111 113 114 115 117 118 119
#>
#> [[3]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 22 27
#> [20] 29 31 32 34 35 36 37 38 43 45 47 50 52 54 56 57 58 59 60
#> [39] 61 62 63 64 65 66 67 69 70 72 73 75 80 83 84 88 89 91 94
#> [58] 100 103 106 108 111 113 114 115 117 119
#>
#> [[4]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 27 31
#> [20] 32 34 35 36 37 38 45 50 52 54 56 57 60 61 62 63 64 66 67
#> [39] 69 72 73 80 83 84 88 89 91 94 100 106 108 111 114 115 117 119
#>
#> [[5]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 27 35 36 38 45 50 52 54
#> [20] 56 57 61 62 63 69 80 84 88 100
#>
#> [[6]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 20 27 31 32
#> [20] 34 35 36 37 38 45 50 52 54 56 57 61 62 63 64 66 69 72 73
#> [39] 80 83 84 88 89 94 100 108 111 114 117
#>
#> [[7]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 18 20 27 29
#> [20] 31 32 34 35 36 37 38 45 50 52 54 56 57 60 61 62 63 64 66
#> [39] 67 69 72 73 80 83 84 88 89 91 93 94 100 108 111 113 114 115 117
#> [58] 119
#>
#> [[8]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 20 27 31 32
#> [20] 34 35 36 38 45 50 52 54 56 57 61 62 63 64 66 69 72 73 80
#> [39] 83 84 88 89 94 100 111 113 114
#>
#> [[9]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 13 14 15 16 20 27 35 36
#> [20] 38 45 50 52 54 56 57 61 62 63 69 80 83 84 88 89 94 100 111
#>
#> [[10]]
#> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20 22
#> [20] 27 29 31 32 34 35 36 37 38 43 45 47 48 50 52 54 56 57 58
#> [39] 59 60 61 62 63 64 65 66 67 69 70 72 73 75 77 79 80 83 84
#> [58] 88 89 90 91 93 94 95 100 101 103 104 106 108 111 113 114 115 117 118
#> [77] 119
#>
#> [[11]]
#> [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
#>