cvLassoBT | R Documentation |
LassoBT
Perform k-fold cross-validation potentially multiple times on permuted version of the data.
cvLassoBT( x, y, lambda = NULL, nlambda = 100L, lambda.min.ratio = ifelse(nobs < nvars, 0.01, 1e-04), nfolds = 5L, nperms = 1L, mc.cores = 1L, ... )
x |
input matrix of dimension nobs by nvars; each row is an observation vector. |
y |
response variable; shoud be a numeric vector. |
lambda |
user supplied |
nlambda |
the number of lambda values. Must be at least 3. |
lambda.min.ratio |
smallest value in |
nfolds |
number of folds. Default is 5. |
nperms |
the number of permuted datasets to apply k-folds corss-validation to. Default is 1 so we carry out vanilla cross-validation. |
mc.cores |
the number of cores to use. Only applicable when not in Windows as it uses the parallel package to parallelise the computations. |
... |
other arguments that can be passed to |
A list with components as below.
lambda
the sequence of lambda
values used
cvm
a matrix of error estimates (with squared error loss). The rows correspond
to different lambda
values whilst the columns correspond to different iterations
BT_fit
a "BT
" object from a fit to the full data.
cv_opt
a two component vector giving the cross-validation optimal lambda
index
and iteration
cv_opt_err
the minimal cross-validation error.
x <- matrix(rnorm(100*250), 100, 250) y <- x[, 1] + x[, 2] - x[, 1]*x[, 2] + x[, 3] + rnorm(100) out <- cvLassoBT(x, y, iter_max=10, nperms=2)
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