grid_lasso_logistic: Computed solution paths for binary response model

In bgs25/SubsetGridRegression: Subset Grid Regression for Lasso, Ridge and Logistic Lasso Regression Models with Potential Prior Information

Description

Computes solutions for grid lasso-penalised logistic regression (wrapper for glmnet)

Usage

grid_lasso_logistic(
  x = NULL,
  y,
  var_order = NULL,
  lambda = NULL,
  nlambda = 100L,
  grid.size = p,
  grid.size.truncate = grid.size,
  lambda.min.ratio = ifelse(n < p, 0.01, 1e-04),
  thresh = 1e-10,
  maxit = 1e+05
)

Arguments

x	Design matrix, n x p
y	Vector of responses, length n
var_order	For user-specified ordering of variables. Indices start at 0, start with least important variable and end with most. By default order will be induced from scaling of columns in design matrix
lambda	For user-specified sequence of tuning parameter lambda
nlambda	Length of automatically generated sequence of tuning parameters lambda
grid.size	Number of subsets of variables for which a solution path will be computed for
grid.size.truncate	Not for user modification and is only altered when called from cv_grid_lasso
lambda.min.ratio	Ratio of max/min lambda for automatically generated sequence of tuning parameters lambda
thresh	Convergence threshold for coordinate descent for difference in objective values between successive iterations
maxit	Maximum number of iterations for coordinate descent routine

Value

A list of glmnet model objects

Examples

set.seed(1)
X = matrix(0, 50, 500)
betavec = c(rep(1,5),rep(0,495))
X[ , 1:5 ] = matrix(rnorm(250), 50, 5)
Y = as.vector(X %*% betavec)
Y = Y + rnorm(50)
Y = as.numeric(Y >= mean(Y))
X = X + matrix(rnorm(50*500), 50, 500)
mod1 = grid_lasso_logistic(X, Y, grid.size = 25)

bgs25/SubsetGridRegression documentation built on Dec. 19, 2021, 8:50 a.m.