Make predictions using the exclusive lasso. Similar to predict.glmnet.
coef(...) is a wrapper around predict(..., type="coefficients").
# S3 method for ExclusiveLassoFit coef(object, lambda = s, s = NULL, exact = FALSE, group_threshold = FALSE, ...) # S3 method for ExclusiveLassoFit predict( object, newx, lambda = s, s = NULL, type = c("link", "response", "coefficients"), group_threshold = FALSE, exact = FALSE, offset, ... )
| object | An |
|---|---|
| lambda | The value of the regularization paramter (\(lambda\)) at which to return the fitted coefficients or predicted values. If not supplied, results for the entire regularization path are returned. Can be a vector. |
| s | An alternate argument that may be used to supply |
| exact | Should the exclusive lasso be re-run for provided values of |
| group_threshold | If |
| ... | Additional arguments passed to |
| newx | New data \(X \in R^{m \times p}\) on which to make predictions. If not supplied, predictions are made on trainng data. |
| type | The type of "prediction" to return. If |
| offset | An offset term used in predictions. If not supplied, all offets are
taken to be zero. If the original fit was made with an offset, |
n <- 200 p <- 500 groups <- rep(1:10, times=50) beta <- numeric(p); beta[1:10] <- 3 X <- matrix(rnorm(n * p), ncol=p) y <- X %*% beta + rnorm(n) exfit <- exclusive_lasso(X, y, groups) coef(exfit, lambda=1)#> 501 x 1 sparse Matrix of class "dgCMatrix" #> #> (Intercept) -0.8556297 #> 1.6177055 #> 1.5167088 #> 1.5576356 #> 1.4557314 #> 1.3429220 #> 1.6525403 #> 1.4577423 #> 1.5505042 #> 1.6672350 #> 1.5469521 #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> . #> .#> [,1] #> [1,] 0.47254886 #> [2,] 5.04540436 #> [3,] 3.85081483 #> [4,] 3.86975131 #> [5,] 3.41044951 #> [6,] -11.02415415 #> [7,] -3.64048632 #> [8,] 0.84734859 #> [9,] 0.91484137 #> [10,] -5.27402297 #> [11,] -0.39952876 #> [12,] 15.48880483 #> [13,] -4.44755518 #> [14,] -1.26347876 #> [15,] 8.95013545 #> [16,] 2.24589700 #> [17,] 1.99259187 #> [18,] -4.79237177 #> [19,] -15.13313313 #> [20,] -4.86348011 #> [21,] -4.37706904 #> [22,] 2.70501526 #> [23,] -6.09681031 #> [24,] 4.94912225 #> [25,] 2.44432918 #> [26,] 3.17673495 #> [27,] -2.86003757 #> [28,] 4.29162528 #> [29,] 0.32223678 #> [30,] 3.77050389 #> [31,] -6.66000532 #> [32,] 0.09047075 #> [33,] -0.99215098 #> [34,] 3.55388375 #> [35,] 4.19342872 #> [36,] 1.98284291 #> [37,] -2.34059964 #> [38,] -1.18307825 #> [39,] 8.61327266 #> [40,] 6.74050579 #> [41,] -0.33866601 #> [42,] -1.91875025 #> [43,] -1.31702917 #> [44,] 7.68592992 #> [45,] -1.64972236 #> [46,] -0.81285410 #> [47,] 6.43237118 #> [48,] -0.12193746 #> [49,] 1.89104319 #> [50,] -4.37134022 #> [51,] -2.67245892 #> [52,] 3.18941850 #> [53,] 2.18097264 #> [54,] 2.85382847 #> [55,] -6.63292420 #> [56,] 1.66931826 #> [57,] 1.63163890 #> [58,] -4.42714265 #> [59,] -4.44410912 #> [60,] 1.91851228 #> [61,] 2.39750877 #> [62,] 8.46343252 #> [63,] -9.54620712 #> [64,] 3.43543970 #> [65,] -3.22943907 #> [66,] -3.93991759 #> [67,] -1.66130886 #> [68,] -0.57741389 #> [69,] -6.09667523 #> [70,] -0.44403343 #> [71,] 4.64949698 #> [72,] 3.81658275 #> [73,] -8.19214944 #> [74,] -3.76640983 #> [75,] -0.20209651 #> [76,] -0.08813113 #> [77,] 1.52605165 #> [78,] 4.19690850 #> [79,] 9.11914243 #> [80,] 8.26153770 #> [81,] -12.94677573 #> [82,] 4.39251489 #> [83,] -1.29658645 #> [84,] -2.50812668 #> [85,] 2.35581350 #> [86,] -6.13685233 #> [87,] 6.60313314 #> [88,] -0.93794557 #> [89,] -0.43430395 #> [90,] -3.12316384 #> [91,] -2.83520741 #> [92,] 5.20006377 #> [93,] -0.37610596 #> [94,] -8.14300526 #> [95,] -8.49184170 #> [96,] -0.31911868 #> [97,] -10.87387683 #> [98,] 2.32531054 #> [99,] 1.13415970 #> [100,] 1.56365102 #> [101,] 2.80957222 #> [102,] -4.28933785 #> [103,] -0.09957953 #> [104,] -3.69449496 #> [105,] -2.73216312 #> [106,] 3.62273667 #> [107,] -1.00202528 #> [108,] 7.39108111 #> [109,] 3.48455726 #> [110,] 7.44041666 #> [111,] -8.31935266 #> [112,] 0.45002535 #> [113,] 4.55363791 #> [114,] 4.54573182 #> [115,] 2.78032953 #> [116,] 1.26106268 #> [117,] -3.54462755 #> [118,] -0.53023501 #> [119,] -3.24936429 #> [120,] -3.18899056 #> [121,] 5.38469234 #> [122,] 3.79904576 #> [123,] 6.27850271 #> [124,] -5.25953790 #> [125,] -2.66628451 #> [126,] 4.86876216 #> [127,] -5.99237063 #> [128,] 1.27887869 #> [129,] 0.42692097 #> [130,] -6.41946363 #> [131,] 2.14105757 #> [132,] -0.44362298 #> [133,] -4.23624492 #> [134,] -0.73645747 #> [135,] 6.22042585 #> [136,] 2.65659598 #> [137,] -7.07728521 #> [138,] 3.74924327 #> [139,] -2.20864705 #> [140,] -6.58030169 #> [141,] 5.58481826 #> [142,] 1.61568624 #> [143,] 2.36627809 #> [144,] -3.83065709 #> [145,] 1.21374710 #> [146,] -4.93889450 #> [147,] -5.39803234 #> [148,] -6.11532094 #> [149,] 2.75353808 #> [150,] 8.18100293 #> [151,] 0.29739668 #> [152,] -0.23586708 #> [153,] -5.43016288 #> [154,] -1.63182104 #> [155,] 9.23067617 #> [156,] -1.69699684 #> [157,] 12.75337736 #> [158,] 9.12022155 #> [159,] -5.88376162 #> [160,] 1.39665210 #> [161,] -1.83803987 #> [162,] 1.67764824 #> [163,] -9.79214240 #> [164,] 2.53719520 #> [165,] 0.42369412 #> [166,] -0.78505885 #> [167,] -0.23157177 #> [168,] 13.90286753 #> [169,] 11.03262775 #> [170,] -9.33342634 #> [171,] -5.90057250 #> [172,] 2.57447438 #> [173,] -10.45479716 #> [174,] -2.75092093 #> [175,] -5.42342954 #> [176,] -6.79782719 #> [177,] -3.01266611 #> [178,] -13.10772763 #> [179,] 1.12508714 #> [180,] 14.05695125 #> [181,] 1.10369774 #> [182,] 8.03950018 #> [183,] -2.90392063 #> [184,] -3.32725396 #> [185,] 5.01334697 #> [186,] 3.89815758 #> [187,] 1.42807095 #> [188,] -6.23357587 #> [189,] -1.15326796 #> [190,] -4.72568998 #> [191,] -0.47751063 #> [192,] -4.98765712 #> [193,] 6.90098602 #> [194,] 5.09570336 #> [195,] 6.74125193 #> [196,] -6.38915339 #> [197,] -5.44107262 #> [198,] 3.64343174 #> [199,] -1.84825908 #> [200,] 5.28245044