SL wrapper for biglasso
SL wrapper for biglasso
SL.biglasso(Y, X, newX, family, obsWeights, penalty = "lasso", alg.logistic = "Newton", screen = "SSR", alpha = 1, nlambda = 100, eval.metric = "default", ncores = 1, nfolds = 5, ...)
Y |
Outcome variable |
X |
Training dataframe |
newX |
Test dataframe |
family |
Gaussian or binomial |
obsWeights |
Observation-level weights |
penalty |
The penalty to be applied to the model. Either "lasso" (default), "ridge", or "enet" (elastic net). |
alg.logistic |
The algorithm used in logistic regression. If "Newton" then the exact hessian is used (default); if "MM" then a majorization-minimization algorithm is used to set an upper-bound on the hessian matrix. This can be faster, particularly in data-larger-than-RAM case. |
screen |
"SSR" (default) is the sequential strong rule; "SEDPP" is the (sequential) EDPP rule. "SSR-BEDPP", "SSR-Dome", and "SSR-Slores" are our newly proposed screening rules which combine the strong rule with a safe rule (BEDPP, Dome test, or Slores rule). Among the three, the first two are for lasso-penalized linear regression, and the last one is for lasso-penalized logistic regression. "None" is to not apply a screening rule. |
alpha |
The elastic-net mixing parameter that controls the relative contribution from the lasso (l1) and the ridge (l2) penalty. |
nlambda |
The number of lambda values to check. Default is 100. |
eval.metric |
The evaluation metric for the cross-validated error and
for choosing optimal |
ncores |
The number of cores to use for parallel execution across a
cluster created by the |
nfolds |
The number of cross-validation folds. Default is 5. |
... |
Any additional arguments, not currently used. |
Zeng Y, Breheny P (2017). biglasso: Extending Lasso Model Fitting to Big Data. https://CRAN.R-project.org/package=biglasso.
## Not run:
data(Boston, package = "MASS")
Y = Boston$medv
# Remove outcome from covariate dataframe.
X = Boston[, -14]
set.seed(1)
# Sample rows to speed up example.
row_subset = sample(nrow(X), 30)
# Subset rows and columns & use only 2 folds to speed up example.
sl = SuperLearner(Y[row_subset], X[row_subset, 1:2, drop = FALSE],
family = gaussian(), cvControl = list(V = 2),
SL.library = "SL.biglasso")
sl
# example for predictions on the full dataset
pred = predict(sl, X)
summary(pred$pred)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.