timeLagLasso.cv: Cross-validation function for timeLagLasso

In orderedLasso: Ordered Lasso and Time-Lag Sparse Regression

Description

Uses cross-validation to estimate the regularization parameter for timeLagLasso model

Usage

timeLagLasso.cv(x, y, maxlag, lamlist = NULL, minlam = NULL,
  maxlam = NULL, nlam = 10, flmin = 0.01, flmax = 1, intercept = TRUE,
  standardize = TRUE, method = c("Solve.QP", "GG"),
  strongly.ordered = TRUE, nfolds = 10, folds = NULL, maxiter = 500,
  inneriter = 100, iter.gg = 100, trace = FALSE, epsilon = 1e-04)

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)
maxlag	Maximum time-lag variable chosen by user
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
flmin	Fraction of maxlam minlam= flmin*maxlam. If computation is slow, try increasing flmin to focus on the sparser part of the path; default 1e-2
flmax	Multiplication of maxlam maxlam = flmax * maxlam; default 1
intercept	True if there is an intercept in the model.
standardize	Standardize the data matrix x.
method	Two options available, Solve.QP and Generalized Gradient. Details about two options can be seen in the orderedLasso description.
strongly.ordered	An option which allows users to order the coefficients in absolute value.
nfolds	Number of cross-validation folds
folds	(Optional) user-supplied cross-validation folds. If provided, nfolds is ignored.
maxiter	maximum iterations run by time-lag lasso. Initialized to 500.
inneriter	maximum iterations run by orderedLasso. Initialized to 100.
iter.gg	Maximum iterations run by generalized gradient. Intialized to 100
trace	Output option; trace = TRUE gives verbose output.
epsilon	Error tolerance parameter for convergence criterion; default 1e-5

Value

lamlist	Vector of lambda values tried
cv.err	Estimate of cross-validation error
cv.se	Estimated standard error of cross-validation estimate
lamhat	lambda value minimizing cv.err
folds	Indices of folds used in cross-validation
lamhat.1se	largest lambda value with cv.err less than or equal to min(cv.err)+ SE
nonzero	Vector giving number of non-zero coefficients for each lambda value
call	The call to timeLagLasso.cv

Examples

set.seed(3)
n = 50
maxlag = 5
num_rows_needed = n + maxlag + 1
sigma = 4
x = matrix(rnorm(num_rows_needed * 4), nrow = num_rows_needed)
x_new = time_lag_matrix(x, maxlag)
b = c(3,1,1,0,0,
      4,1,0,0,0,
      3,2,1,0,0,
      1,0,0,0,0)
y = x_new %*% b + sigma* rnorm(nrow(x_new))
y = as.vector(y)
y = c(y, rnorm(maxlag + 1))
cvmodel = timeLagLasso.cv(x= x, y = y, maxlag = 5, method = "Solve.QP")

Example output

Loading required package: Matrix
build the matrix for timeLagLasso.cv,  6 observations in y are deleted

orderedLasso documentation built on May 2, 2019, 6:36 a.m.