Description Usage Arguments Details Author(s) References See Also Examples
Outputs predicted response values for new user input observations at a specified lambda
value
1 | predictSGL(x, newX, lam)
|
x |
fitted |
newX |
covariate matrix for new observations whose responses we wish to predict |
lam |
the index of the lambda value for the model with which we desire to predict |
Predicted outcomes are given
Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Maintainer: Noah Simon <nrsimon@uw.edu>
Simon, N., Friedman, J., Hastie T., and Tibshirani, R. (2011)
A Sparse-Group Lasso,
http://faculty.washington.edu/nrsimon/SGLpaper.pdf
1 2 3 4 5 6 7 8 9 | n = 50; p = 100; size.groups = 10
index <- ceiling(1:p / size.groups)
X = matrix(rnorm(n * p), ncol = p, nrow = n)
beta = (-2:2)
y = X[,1:5] %*% beta + 0.1*rnorm(n)
data = list(x = X, y = y)
Fit = SGL(data, index, type = "linear")
X.new = matrix(rnorm(n * p), ncol = p, nrow = n)
predictSGL(Fit, X.new, 5)
|
[,1]
[1,] 1.571113175
[2,] 0.396861268
[3,] -0.550406961
[4,] -0.934004112
[5,] -0.361061100
[6,] 0.143259938
[7,] 0.946538630
[8,] 0.333117808
[9,] 0.140766844
[10,] -1.198808012
[11,] -0.601096979
[12,] -1.359269664
[13,] 0.109621210
[14,] -0.669150639
[15,] -1.592105436
[16,] -1.243260498
[17,] -0.392497383
[18,] -0.308278763
[19,] -1.603408158
[20,] 0.068144965
[21,] 0.355428893
[22,] -0.445159381
[23,] -1.717634199
[24,] -0.247810698
[25,] -0.003296874
[26,] -0.912611758
[27,] 0.615287378
[28,] -1.567959436
[29,] -0.399492720
[30,] -1.395916446
[31,] -2.516359023
[32,] -1.061197532
[33,] -2.160488589
[34,] -1.076110802
[35,] -1.085599167
[36,] -1.710408400
[37,] -0.653489223
[38,] 0.568259139
[39,] 0.153331595
[40,] 0.243242745
[41,] -0.806968492
[42,] -0.382104972
[43,] -0.305069368
[44,] 0.045994599
[45,] 0.699612475
[46,] -0.512533319
[47,] -0.611067421
[48,] -1.063862540
[49,] -1.418699248
[50,] 0.864976503
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