Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/predict.creNet.R

Outputs predicted response values for new user input observations at a specified `lambda`

values

1 | ```
predict(obj, newX, lam=NULL, standardize=c("train","self"))
``` |

`obj` |
A fitted object of class |

`newX` |
A matrix of new covariates |

`lam` |
The indexes of lambda values to be used for prediction |

`standardize` |
The standardization method for |

The matrix `newX`

should have the same number of columns as the covariate matrix used to obtain `obj`

. The argument `standardize`

specifies how `newX`

should be standardized. For the choice `"train"`

, the means and variances attached to `obj`

are used. For the choice `"self"`

, the own means and variances of `newX`

are used.

A vector or matrix of predicted responses. Each column corresponds to a value of lambda.

Kourosh Zarringhalam and David Degras

Modified from SGL package: Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani

Maintainer: Kourosh Zarringhalam <[email protected]>

Simon, N., Friedman, J., Hastie T., and Tibshirani, R. (2011)
*A Sparse-Group Lasso*,

http://web.stanford.edu/~hastie/Papers/SGLpaper.pdf

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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)
y = ifelse((exp(y) / (1 + exp(y))) > 0.5, 1, 0)
data = list(x = X, y = y)
weights = rep(1, size.groups)
Fit = creSGL(data, index, weights, type = "logit", maxit = 1000, thresh = 0.001,
min.frac = 0.05, nlam = 100, gamma = 0.8, standardize = TRUE, verbose = FALSE,
step = 1, reset = 10, alphas = 0.05, lambdas = NULL)
X.new = matrix(rnorm(n * p), ncol = p, nrow = n)
predict(Fit, X.new, 5)
``` |

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