orderedLasso (version 1.7.1)

orderedLasso.cv: Cross-validation function for the ordered lasso

Description

Uses cross-validation to estimate the regularization parameter for the ordered lasso model.

Usage

orderedLasso.cv(x, y, lamlist = NULL, minlam = NULL, maxlam = NULL,
  nlam = 50, flmin = 5e-04, strongly.ordered = FALSE, intercept = TRUE,
  standardize = TRUE, nfolds = 10, folds = NULL, niter = 500,
  iter.gg = 100, method = c("Solve.QP", "GG"), trace = FALSE,
  epsilon = 1e-05)

Arguments

x

A matrix of predictors, where the rows are the samples and the columns are the predictors

y

A vector of observations, where length(y) equals nrow(x)

lamlist

Optional vector of values of lambda (the regularization parameter)

minlam

Optional minimum value for lambda

maxlam

Optional maximum value for lambda

nlam

Number of values of lambda to be tried. Default nlam = 50.

flmin

Fraction of maxlam minlam= flmin*maxlam. If computation is slow, try increasing flmin to focus on the sparser part of the path. Default flmin = 1e-4.

strongly.ordered

An option which allows users to order the coefficients in absolute value.

intercept

True if there is an intercept in the model.

standardize

Standardize the data matrix.

nfolds

Number of cross-validation folds.

folds

(Optional) user-supplied cross-validation folds. If provided, nfolds is ignored.

niter

Number of iterations the ordered lasso takes to converge. Default nither = 500.

iter.gg

Number of iterations of generalized gradient method; default 100

method

Two options available, Solve.QP and Generalized Gradient. Details of two options can be seen in the orderedLasso description.

trace

Output option; trace=TRUE gives verbose output

epsilon

Error tolerance parameter for convergence criterion; default 1e-5

Examples

# NOT RUN {
set.seed(3)
n = 50
b = c(4,3,1,0)
p = length(b)
x = matrix(rnorm(n*p),nrow = n)
sigma = 5
y = x %*% b + sigma * rnorm(n, 0, 1)
cvmodel = orderedLasso.cv(x,y, intercept = FALSE, trace = TRUE, 
          method = "Solve.QP", strongly.ordered = TRUE)
print(cvmodel)
plot(cvmodel)
# }