stepglm_wrapper: Wrapper for fitting a forward stepwise logistic regression...

In nlpred: Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples

Description

Compatible learner wrappers for this package should have a specific format. Namely they should take as input a list called train that contains named objects $Y and $X, that contain, respectively, the outcomes and predictors in a particular training fold. Other options may be passed in to the function as well. The function must output a list with the following named objects: test_pred = predictions of test$Y based on the learner fit using train$X; train_pred = prediction of train$Y based on the learner fit using train$X; model = the fitted model (only necessary if you desire to look at this model later, not used for internal computations); train_y = a copy of train$Y; test_y = a copy of test$Y.

Usage

stepglm_wrapper(train, test)

Arguments

train	A list with named objects Y and X (see description).
test	A list with named objects Y and X (see description).

Details

This particular wrapper implements a forward stepwise logistic regression using glm and step. We refer readers to the original package's documentation for more details.

Value

A list with named objects (see description).

Examples

# simulate data
# make list of training data
train_X <- data.frame(x1 = runif(50))
train_Y <- rbinom(50, 1, plogis(train_X$x1))
train <- list(Y = train_Y, X = train_X)
# make list of test data
test_X <- data.frame(x1 = runif(50))
test_Y <- rbinom(50, 1, plogis(train_X$x1))
test <- list(Y = test_Y, X = test_X)
# fit stepwise glm
step_wrap <- stepglm_wrapper(train = train, test = test)

nlpred documentation built on Feb. 24, 2020, 1:11 a.m.