R Regression: stepwise regression essentials

# Ref: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/

library(tidyverse)
library(caret)
library(leaps)

# Computing stepwise regression
library(MASS)
# Fit the full model 
full.model <- lm(Fertility ~., data = swiss)
# Stepwise regression model
step.model <- stepAIC(full.model, direction = "both", 
                      trace = FALSE)
summary(step.model)

models <- regsubsets(Fertility~., data = swiss, nvmax = 5,
                     method = "seqrep")
summary(models)

# Set seed for reproducibility
set.seed(123)
# Set up repeated k-fold cross-validation
train.control <- trainControl(method = "cv", number = 10)
# Train the model
step.model <- train(Fertility ~., data = swiss,
                    method = "leapBackward", 
                    tuneGrid = data.frame(nvmax = 1:5),
                    trControl = train.control
)
step.model$results

# best tuning values (nvmax)
step.model$bestTune

summary(step.model$finalModel)

# regression coefficients of the final model (id = 4)
coef(step.model$finalModel, 4)
lm(Fertility ~ Agriculture + Education + Catholic + Infant.Mortality, 
   data = swiss)

# ALTERNATIVE
library(MASS)
res.lm <- lm(Fertility ~., data = swiss)
step <- stepAIC(res.lm, direction = "both", trace = FALSE)
step

# Train the model
step.model <- train(Fertility ~., data = swiss,
                    method = "lmStepAIC", 
                    trControl = train.control,
                    trace = FALSE
)
# Model accuracy
step.model$results
# Final model coefficients
step.model$finalModel
# Summary of the model
summary(step.model$finalModel)

Referensi

• http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/

Pranala Menarik

• R