Wrapper for glm
Wrapper for generalized linear models via glm().
Note that for outcomes bounded by [0, 1] the binomial family can be used in addition to gaussian.
SL.glm(Y, X, newX, family, obsWeights, model = TRUE, ...)
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. |
Fox, J. (2015). Applied regression analysis and generalized linear models. Sage Publications.
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.glm"))
print(sl)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.