FSGL: Fit a Linear Model with a Combination of Lasso and Group...

In xhnnnnn/FSGL: Fit a Linear Model with a Combination of Lasso and Group Lasso Regularization. The calculation will more fast than 'SGL' package.

Description

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, logistic and Cox models.

Usage

FSGL(
  data,
  index,
  maxit = 1000,
  thresh = 0.001,
  min.frac = 0.1,
  nlam = 20,
  gamma = 0.8,
  standardize = TRUE,
  verbose = FALSE,
  step = 1,
  reset = 10,
  alpha = 0.95,
  lambdas = NULL,
  UseUpperBound = FALSE
)

Arguments

data	data should be a list of $x$ and $y$, x is a data matrix (n x p) and y is a vector
index	A p-vector indicating group membership of each covariate
maxit	Maximum number of iterations to convergence
thresh	Convergence threshold for change in beta
min.frac	The minimum value of the penalty parameter, as a fraction of the maximum value
nlam	Number of lambda to use in the regularization path
gamma	Fitting parameter used for tuning backtracking (between 0 and 1)
standardize	Logical flag for variable standardization prior to fitting the model
verbose	Logical flag for whether or not step number will be output
step	Fitting parameter used for inital 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)
alpha	The mixing parameter. alpha = 1 is the lasso penalty. alpha = 0 is the group lasso penalty.
lambdas	A user specified sequence of lambda values for fitting. We recommend leaving this NULL and letting SGL self-select values
UseUpperBound	A logical flag for using upper bound

Value

An object of type "FSGL"

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)

X.transform

A list used in $predict$ which gives the empirical mean and variance of the x matrix used to build the model

Examples

library(FSGL)

n = 100; p = 20; 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 = FSGL(data, index)

xhnnnnn/FSGL documentation built on Jan. 7, 2022, 6:51 p.m.