olasso_cv {natural} | R Documentation |
Cross-validation for organic lasso
Description
Provide organic lasso estimate (of the error standard deviation) using cross-validation to select the tuning parameter value
Usage
olasso_cv(x, y, lambda = NULL, intercept = TRUE, nlam = 100,
flmin = 0.01, nfold = 5, foldid = NULL, thresh = 1e-08)
Arguments
x |
An |
y |
A response vector of size |
lambda |
A user specified list of tuning parameter. Default to be NULL, and the program will compute its own |
intercept |
Indicator of whether intercept should be fitted. Default to be |
nlam |
The number of |
flmin |
The ratio of the smallest and the largest values in |
nfold |
Number of folds in cross-validation. Default value is 5. If each fold gets too view observation, a warning is thrown and the minimal |
foldid |
A vector of length |
thresh |
Threshold value for underlying optimization algorithm to claim convergence. Default to be |
Value
A list object containing:
n
andp
:The dimension of the problem.
lambda
:The path of tuning parameter used.
beta
:Estimate of the regression coefficients, in the original scale, corresponding to the tuning parameter selected by cross-validation.
a0
:Estimate of intercept
mat_mse
:The estimated prediction error on the test sets in cross-validation. A matrix of size
nlam
bynfold
cvm
:The averaged estimated prediction error on the test sets over K folds.
cvse
:The standard error of the estimated prediction error on the test sets over K folds.
ibest
:The index in
lambda
that attains the minimal mean cross-validated error.foldid
:Fold assignment. A vector of length
n
.nfold
:The number of folds used in cross-validation.
sig_obj
:Organic lasso estimate of the error standard deviation, selected by cross-validation.
sig_obj_path
:Organic lasso estimates of the error standard deviation. A vector of length
nlam
.type
:whether the output is of a natural or an organic lasso.
See Also
Examples
set.seed(123)
sim <- make_sparse_model(n = 50, p = 200, alpha = 0.6, rho = 0.6, snr = 2, nsim = 1)
ol_cv <- olasso_cv(x = sim$x, y = sim$y[, 1])