Description Arguments Details Value
Run bootstrap model fits to assess coefficient distributions.
times |
number of bootstrap samples to run |
bag.fraction |
fraction of observations to sample for each bootstrap run |
replace |
whether to sample with or without replacement |
nfolds |
number of CV folds to run within each bootstrap fit |
upper.limits |
maximum coffecient value for each fit |
lower.limits |
minimum coffecient value for each fit |
alpha |
mixing paramater between LASSO and Ridge regression. Default 1. |
... |
other parameters passed on to cv.glmnet |
True boostratp samples should be run with bag.fraction=1
and
replace=TRUE
.
a list with two elements: pvals and coefficients. The former is a vector indicating what proportion of bootstrap model fits each coefficient returned a zero. The latter an nRuns x nVars matrix containing the coefficients of each run.
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