Onnowpurbo: Created page with "# Ref: http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/ library(tidyverse) library(caret) # Load the..."

2019-12-02T01:43:42Z

Created page with "# Ref: http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/ library(tidyverse) library(caret) # Load the..."

New page

# Ref: http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/

library(tidyverse)
library(caret)

# Load the data
data("Boston", package = "MASS")
# Split the data into training and test set
set.seed(123)
training.samples <- Boston$medv %>%
createDataPartition(p = 0.8, list = FALSE)
train.data <- Boston[training.samples, ]
test.data <- Boston[-training.samples, ]

# Build the model
model1 <- lm(medv ~., data = train.data)
# Make predictions
predictions <- model1 %>% predict(test.data)
# Model performance
data.frame(
RMSE = RMSE(predictions, test.data$medv),
R2 = R2(predictions, test.data$medv)
)

# Detecting multicollinearity
car::vif(model1)

# Dealing with multicollinearity
# Build a model excluding the tax variable
model2 <- lm(medv ~. -tax, data = train.data)
# Make predictions
predictions <- model2 %>% predict(test.data)
# Model performance
data.frame(
RMSE = RMSE(predictions, test.data$medv),
R2 = R2(predictions, test.data$medv)
)

==Referensi==

* http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/

==Pranala Menarik==

* [[R]]

R Regression: multicollinearity essentials and vif - Revision history

Onnowpurbo: Created page with "# Ref: http://www.sthda.com/english/articles/39-regression-model-diagnostics/160-multicollinearity-essentials-and-vif-in-r/ library(tidyverse) library(caret) # Load the..."