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

Fits and cross-validates a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter, and a parameter value is chosen by cross-validation. Fits linear and logistic models.

1 2 3 4 | ```
cvSGL(data, index = NULL, weights=NULL, type = c("linear","logit"), alphas = seq(0,1,.1),
nlam = 20, standardize = c("train","self","all","no"), nfold = 10, measure = c("ll","auc"),
maxit = 1000, thresh = 0.001, min.frac = 0.05, gamma = 0.8, step = 1, reset = 10, ncores = 1,
lambdas = NULL, verbose = FALSE)
``` |

`data` |
A list with components $x$, an input matrix of dimension $(n,p)$, and $y$, a response vector of length $n$. For |

`index` |
A $p$-vector indicating group membership of each covariate |

`weights` |
Optional vector of weights for the group penalties |

`type` |
Model type: "linear" or "logit" |

`alphas` |
Vector of mixing parameters. |

`nlam` |
Number of lambda values in the regularization path |

`standardize` |
Type of standardization for full data and CV folds. |

`nfold` |
Number of folds of the cross-validation loop |

`measure` |
Performance measure used to select the best values |

`maxit` |
Maximum number of iterations to convergence |

`thresh` |
Convergence threshold for change in beta |

`min.frac` |
Minimum value of the penalty parameter, as a fraction of the maximum value |

`gamma` |
Fitting parameter used for tuning backtracking (between 0 and 1) |

`step` |
Fitting parameter used for initial backtracking step size (between 0 and 1) |

`reset` |
Fitting parameter used for taking advantage of local strong convexity in Nesterov momentum (number of iterations before momentum term is reset) |

`ncores` |
Number of computer cores to use in computations |

`lambdas` |
User-specified sequence of lambda values for fitting. We recommend leaving this NULL and letting cvSGL self-select values |

`verbose` |
Logical flag for whether or not step number will be output |

The function executes `SGL`

`nfold`

+1 times; the initial run is to find the `lambda`

sequence, subsequent runs are used to compute the cross-validated error rate and its standard deviation. By default, `weights`

are the square roots of group sizes.

An object of class `"cv.creNet"`

and `"creNet"`

with components

`fit` |
The fitted model using the best values of |

`best.lambda` |
Index and value of the best element in |

`best.alpha` |
Index and value of the best element in |

`lldiff` |
Cross-validation (negative) log likelihood for all |

`llSD` |
Approximate standard deviations of |

`AUC` |
Area Under the Curve |

`lambdas` |
Values of |

`alphas` |
User-specified argument |

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 | ```
set.seed(1)
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)
weights = rep(1, size.groups)
cvFit = cvcreSGL(data, index, weights, type = "linear", maxit = 1000, thresh = 0.001, min.frac = 0.05, nlam = 100, gamma = 0.8, nfold = 10, standardize = TRUE, verbose = FALSE, step = 1, reset = 10, alpha = 0.05, lambdas = NULL)
``` |

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