SL.glm.gaussianlog: Generalized linear model with Gaussian family and log link
In wuziyueemory/twostageSL: Two-Stage Super Learner Prediction Function

Description Usage Arguments Value Examples

The glm.fit algorithm is notoriously unstable for this fitting. If convergence issues arise, try different starting values.

1	SL.glm.gaussianlog(Y, X, newX, family, obsWeights, link = "log", ...)

`Y`	A numeric outcome variable
`X`	A `data.frame` of covariates constituting the training sample
`newX`	A `data.frame` with the same column names and format as `X` constituting the validation sample.
`family`	Gaussian only
`obsWeights`	Observation-level weights (not currently used)
`link`	Link function
`...`	Other arguments (not currently used)

pred: Predicted outcomes based on predictors in newX
fit: A list with named entries object (the fitted glm regression object)

# load cost data
data(cost_data)
# fit gaussian glm with log link
fit_glm.gaussianlog <- SL.glm.gaussianlog(Y = cost_data$totalcost, X = cost_data[, c("female", "race")],
                              newX = cost_data[, c("female", "race")])
# get back predictions
pred_glm.gaussianlog <- predict(fit_glm.gaussianlog$fit, newdata = cost_data[,c("female", "race")])