tscv.sglfit | R Documentation |
Does k-fold time series cross-validation for sg-LASSO regression model.
The function runs sglfit K+1
times; the first to get the path solution in λ sequence, the rest to compute the fit with each of the test observation k ∈ K
The average error and standard deviation over the folds is computed, and the optimal regression coefficients are returned for lam.min
and lam.1se
. Solutions are computed for a fixed γ
tscv.sglfit(x, y, lambda = NULL, gamma = 1.0, gindex = 1:p, K = 20, l = 5, parallel = FALSE, seed = NULL, ...)
x |
T by p data matrix, where T and p respectively denote the sample size and the number of regressors. |
y |
T by 1 response variable. |
lambda |
a user-supplied lambda sequence. By leaving this option unspecified (recommended), users can have the program compute its own λ sequence based on |
gamma |
sg-LASSO mixing parameter. γ = 1 gives LASSO solution and γ = 0 gives group LASSO solution. |
gindex |
p by 1 vector indicating group membership of each covariate. |
K |
number of observations drawn for the test set. Default set to |
l |
the gap used to drop observations round the test set data point. Default set to |
parallel |
if |
seed |
set a value for seed to control results replication, i.e. |
... |
Other arguments that can be passed to sglfit. |
tscv.sglfit object.
Jonas Striaukas
set.seed(1) x = matrix(rnorm(100 * 20), 100, 20) beta = c(5,4,3,2,1,rep(0, times = 15)) y = x%*%beta + rnorm(100) gindex = sort(rep(1:4,times=5)) tscv.sglfit(x = x, y = y, gindex = gindex, gamma = 0.5, standardize = FALSE, intercept = FALSE) ## Not run: # Parallel require(doMC) registerDoMC(cores = 2) x = matrix(rnorm(1000 * 20), 1000, 20) beta = c(5,4,3,2,1,rep(0, times = 15)) y = x%*%beta + rnorm(1000) gindex = sort(rep(1:4,times=5)) system.time(tscv.sglfit(x = x, y = y, gindex = gindex, gamma = 0.5, standardize = FALSE, intercept = FALSE)) system.time(tscv.sglfit(x = x, y = y, gindex = gindex, gamma = 0.5, standardize = FALSE, intercept = FALSE, parallel = TRUE)) ## End(Not run)
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