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http://www./english/articles/40-regression-analysis/164-interaction-effect-in-multiple-regression-essentials/ This chapter describes how to compute multiple linear regression with interaction effects. Previously, we have described how to build a multiple linear regression model (Chapter @ref(linear-regression)) for predicting a continuous outcome variable (y) based on multiple predictor variables (x). For example, to predict sales, based on advertising budgets spent on youtube and facebook, the model equation is The above equation, also known as additive model, investigates only the main effects of predictors. It assumes that the relationship between a given predictor variable and the outcome is independent of the other predictor variables (James et al. 2014,P. Bruce and Bruce (2017)). Considering our example, the additive model assumes that, the effect on sales of youtube advertising is independent of the effect of facebook advertising. This assumption might not be true. For example, spending money on facebook advertising may increase the effectiveness of youtube advertising on sales. In marketing, this is known as a synergy effect, and in statistics it is referred to as an interaction effect (James et al. 2014). In this chapter, you’ll learn:
Contents: EquationThe multiple linear regression equation, with interaction effects between two predictors (x1 and x2), can be written as follow:
Considering our example, it becomes:
This can be also written as:
or as:
In the following sections, you will learn how to compute the regression coefficients in R. Loading Required R packages
library(tidyverse)library(caret) Preparing the dataWe’ll use the We’ll randomly split the data into training set (80% for building a predictive model) and test set (20% for evaluating the model). # Load the datadata("marketing", package = "datarium")# Inspect the datasample_n(marketing, 3)## youtube facebook newspaper sales ## 58 163.4 23.0 19.9 15.8 ## 157 112.7 52.2 60.6 18.4 ## 81 91.7 32.0 26.8 14.2 # Split the data into training and test setset.seed(123)training.samples <- marketing$sales %>% createDataPartition(p = 0.8, list = FALSE)train.data <- marketing[training.samples, ]test.data <- marketing[-training.samples, ] ComputationAdditive modelThe standard linear regression model can be computed as follow: # Build the modelmodel1 <- lm(sales ~ youtube + facebook, data = train.data)# Summarize the modelsummary(model1) ## ## Call: ## lm(formula = sales ~ youtube + facebook, data = train.data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -10.481 -1.104 0.349 1.423 3.486 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 3.43446 0.40877 8.4 2.3e-14 *** ## youtube 0.04558 0.00159 28.7 < 2e-16 *** ## facebook 0.18788 0.00920 20.4 < 2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 2.11 on 159 degrees of freedom ## Multiple R-squared: 0.89, Adjusted R-squared: 0.889 ## F-statistic: 644 on 2 and 159 DF, p-value: <2e-16 # Make predictionspredictions <- model1 %>% predict(test.data)# Model performance# (a) Prediction error, RMSERMSE(predictions, test.data$sales) ## [1] 1.58 # (b) R-squareR2(predictions, test.data$sales) ## [1] 0.938 Interaction effectsIn R, you include interactions between variables using the # Build the model# Use this: model2 <- lm(sales ~ youtube + facebook + youtube:facebook, data = marketing)# Or simply, use this: model2 <- lm(sales ~ youtube*facebook, data = train.data)# Summarize the modelsummary(model2) ## ## Call: ## lm(formula = sales ~ youtube * facebook, data = train.data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -7.438 -0.482 0.231 0.748 1.860 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 7.90e+00 3.28e-01 24.06 <2e-16 *** ## youtube 1.95e-02 1.64e-03 11.90 <2e-16 *** ## facebook 2.96e-02 9.83e-03 3.01 0.003 ** ## youtube:facebook 9.12e-04 4.84e-05 18.86 <2e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 1.18 on 158 degrees of freedom ## Multiple R-squared: 0.966, Adjusted R-squared: 0.966 ## F-statistic: 1.51e+03 on 3 and 158 DF, p-value: <2e-16 # Make predictionspredictions <- model2 %>% predict(test.data)# Model performance# (a) Prediction error, RMSERMSE(predictions, test.data$sales) ## [1] 0.963 # (b) R-squareR2(predictions, test.data$sales) ## [1] 0.982 InterpretationIt can be seen that all the coefficients, including the interaction term coefficient, are statistically significant, suggesting that there is an interaction relationship between the two predictor variables (youtube and facebook advertising). Our model equation looks like this:
We can interpret this as an increase in youtube advertising of 1000 dollars is associated with increased sales of Comparing the additive and the interaction modelsThe prediction error RMSE of the interaction model is 0.963, which is lower than the prediction error of the additive model (1.58). Additionally, the R-square (R2) value of the interaction model is 98% compared to only 93% for the additive model. These results suggest that the model with the interaction term is better than the model that contains only main effects. So, for this specific data, we should go for the model with the interaction model. DiscussionThis chapter describes how to compute multiple linear regression with interaction effects. Interaction terms should be included in the model if they are significantly. ReferencesBruce, Peter, and Andrew Bruce. 2017. Practical Statistics for Data Scientists. O’Reilly Media. James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2014. An Introduction to Statistical Learning: With Applications in R. Springer Publishing Company, Incorporated. |
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