SL.lm {SuperLearner}

Wrapper for lm

Description

Wrapper for OLS via lm(), which may be faster than glm().

Usage

SL.lm(Y, X, newX, family, obsWeights, model = TRUE, ...)

Arguments

Y

Outcome variable

X

Training dataframe

newX

Test dataframe

family

Gaussian or binomial

obsWeights

Observation-level weights

model

Whether to save model.matrix of data in fit object. Set to FALSE to save memory.

...

Any remaining arguments, not used.

References

Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.

See Also

predict.SL.lm lm predict.lm SL.speedlm

Examples

data(Boston, package = "MASS")
Y = Boston$medv
# Remove outcome from covariate dataframe.
X = Boston[, -14]

set.seed(1)

sl = SuperLearner(Y, X, family = gaussian(),
                  SL.library = c("SL.mean", "SL.lm"))

print(sl)