Difference between revisions of "R Regression: stepwise regression essentials"

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(Created page with " # 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)...")
 
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Latest revision as of 08:46, 2 December 2019

# 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)


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