cvSGL: Fit and cross-validate a GLM with a combination of lasso and...
In kouroshz/creNet: Fit a GLM with a combination of lasso and overlapping group lasso regularization

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.

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 `type="logit"` $y$ should be binary
`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. `alpha` = 1 is the lasso penalty.
`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 `alphas` and `lambdas`
`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 `alphas` and `lambdas` (class `"creNet"`)
`best.lambda`	Index and value of the best element in `lambdas`
`best.alpha`	Index and value of the best element in `alphas`
`lldiff`	Cross-validation (negative) log likelihood for all `alphas` and `lambdas` (=squared error loss if `type=linear`)
`llSD`	Approximate standard deviations of `lldiff`
`AUC`	Area Under the Curve
`lambdas`	Values of `lambda` used in cross-validation.
`alphas`	User-specified argument `alphas`.

Kourosh Zarringhalam and David Degras

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

Maintainer: Kourosh Zarringhalam <kourosh.zarringhalam@umb.edu>

Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2011) A Sparse-Group Lasso,
http://web.stanford.edu/~hastie/Papers/SGLpaper.pdf

creSGL

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)