cv_grid_ridge: Computes cross-validated solution for ridge regression

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

Description

Cross-validation wrapper for grid_ridge that computes solutions, selects and fits the optimal model.

Usage

cv_grid_ridge(
  x,
  y,
  K = 5,
  var_order = NULL,
  lambda = NULL,
  nlambda = 100L,
  lambda.min.ratio = ifelse(n < p, 0.01, 1e-04),
  grid.size = p,
  lambda.mult = 1e+05,
  fold_assign = NULL
)

Arguments

x	Design matrix, n x p.
y	Vector of responses, length n.
K	Number of folds for cross-validation. Must be at least 2
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
lambda.min.ratio	Ratio of max/min lambda for automatically generated sequence of tuning parameters lambda
grid.size	Number of subsets of variables for which a solution path will be computed for
lambda.mult	Scales the sequence of lambda by a constant
fold_assign	For user-specified vector of assignment of folds for cross-validation. Must be of the form of integer vector with entries in 1 , ... , K.

Value

A list of objects:

• mu – estimated intercept

• beta – coefficient estimate

• cv – a matrix of errors for the models (grid.size times nlambda)

• lambda – sequence of lambda values used

Examples

set.seed(1)
X = matrix(0, 50, 100)
betavec = c(rep(1,5),rep(0,95))
X[ , 1:5 ] = matrix(rnorm(250), 50, 5)
Y = as.vector(X %*% betavec)
Y = Y + rnorm(50)
X = X + matrix(rnorm(50*100), 50, 100)
mod1 = cv_grid_ridge(X, Y, grid.size = 50)

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