SGL: Fit a GLM with a combination of lasso and group lasso with...
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

Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear and logistic models.

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SGL(data, index=NULL, weights=NULL, type = c("linear","logit"), alphas = 0.95, nlam = 20,
	standardize = TRUE, maxit = 1000, thresh = 0.001, min.frac = 0.1, gamma = 0.8, step = 1, reset = 10,
	ncores = 1, lambdas = NULL)

`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. The value 1 yields the lasso penalty. The value 0 yields the group lasso penalty.
`nlam`	Number of lambda values to use in the regularization path
`standardize`	Logical flag for variable standardization prior to fitting the model.
`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)
`lambdas`	Optional sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values

The sequence of models along the regularization path is fit by accelerated generalized gradient descent. If specified, argument weights should be a vector whose length is the number of groups. By default, weights is set to the square root of group sizes.

A single object of class "creNet" or a list of such objects if alphas has length >1. For each value in alphas, the result has components:

`beta`	A p by `nlam` matrix, giving the penalized MLEs for the nlam different models, where the index corresponds to the penalty parameter `lambda`
`lambdas`	The actual sequence of `lambda` values used (penalty parameter)
`type`	Response type (logistic/linear)
`intercept`	For some model types, an intercept is fit
`X.transform`	A list used in `predict` which gives the empirical mean and variance of the x matrix used to build the model
`lambdas`	The sequence of lambda values used for fitting

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

cvSGL, predict.SGL

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 = "linear", 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)