In this article, we will look into what is Interaction, and should we use interaction in our model to get better results or not. Let’s say X1 and X2 are features of a dataset and Y is the class label or output that we are trying to predict. Then, If X1 and X2 interact, this means that the effect of X1 on Y depends on the value of X2 and vice versa then where is the interaction between features of the dataset. Now that we know that if our dataset contains interaction or not. We should also know when to take interaction into account in our model for better precision or accuracy. We are going to implement this using the R language.

**Should We Include Interaction in Our Model?**

There are two questions you should ask before including interaction in your model:

- Does this interaction make sense conceptually?
- Is the interaction term statistically significant? Or, whether or not we believe the slopes of the regression lines are significantly different.

## Implementation in R

Let’s look at the interaction in the linear regression model through an example.

**Dataset****Parameters/Variables:**- Independent Variable(Y): LungCap
- Dependent Variable(X1): Smoke(Yes/No)
- Dependent Variable(X2): Age

**Example **

**Step 1: Load the Data Set**

## R

`# Read in the Lung Cap Data` `LungCapData <- ` `read.table` `(` `file.choose` `(), ` ` ` `header = T,` ` ` `sep = ` `"\t"` `)` ` ` `# Attach LungCapData` `attach` `(LungCapData)` |

**Step 2: Plot the data, using different colors for smoke(red) / non-smoker (blue)**

## R

`# Plot the data, using different ` `# colours for smoke(red)/non-smoke(blue)` `# First, plot the data for ` `# the Non-Smokers, in Blue` `plot` `(Age[Smoke == ` `"no"` `], ` ` ` `LungCap[Smoke == ` `"no"` `], ` ` ` `col = ` `"blue"` `, ` ` ` `ylim = ` `c` `(0, 15), xlim = ` `c` `(0, 20), ` ` ` `xlab = ` `"Age"` `, ylab = ` `"LungCap"` `, ` ` ` `main = ` `"LungCap vs. Age,Smoke"` `)` |

**Output:**

## R

`# Now, add in the points for ` `# the Smokers, in Solid Red Circles` `points` `(Age[Smoke == ` `"yes"` `], ` ` ` `LungCap[Smoke == ` `"yes"` `],` ` ` `col = ` `"red"` `, pch = 16)` |

**Output:**

## R

`# And, add in a legend` `legend` `(1, 15, ` ` ` `legend = ` `c` `(` `"NonSmoker"` `, ` `"Smoker"` `), ` ` ` `col = ` `c` `(` `"blue"` `, ` `"red"` `), ` ` ` `pch = ` `c` `(1, 16), bty = ` `"n"` `)` |

**Output:**

**Step 3. Fit a Reg Model, using Age, Smoke, and their INTERACTION and Add in the regression lines**

## R

`# Fit a Reg Model, using Age,` `# Smoke, and their INTERACTION` `model1 <- ` `lm` `(LungCap ~ Age*Smoke)` `coef` `(model1)` |

**Output:**

(Intercept) Age Smokeyes Age:Smokeyes 1.05157244 0.55823350 0.22601390 -0.05970463

## R

`# Note, that the "*" fits a model with ` `# Age, Smoke and AgeXSmoke INT.` `# Note, also that the same model ` `# can be fit using the ":"` `model1 <- ` `lm` `(LungCap ~ Age + Smoke + Age:Smoke)` ` ` `# Ask for a summary of the model` `summary` `(model1)` |

**Output:**

Call: lm(formula = LungCap ~ Age + Smoke + Age:Smoke) Residuals: Min 1Q Median 3Q Max -4.8586 -1.0174 -0.0251 1.0004 4.1996 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.05157 0.18706 5.622 2.7e-08 *** Age 0.55823 0.01473 37.885 < 2e-16 *** Smokeyes 0.22601 1.00755 0.224 0.823 Age:Smokeyes -0.05970 0.06759 -0.883 0.377 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.515 on 721 degrees of freedom Multiple R-squared: 0.6776, Adjusted R-squared: 0.6763 F-statistic: 505.1 on 3 and 721 DF, p-value: < 2.2e-16

**Step 4: Let’s add in the regression lines from our model using the abline command**

## R

`# Now, let's add in the regression` `# lines from our mode using the ` `# abline command for the Non-Smokers, in Blue` `abline` `(a = 1.052, b = 0.558,` ` ` `col = ` `"blue"` `, lwd = 3)` |

**Output:**

## R

`# And now, add in the line for Smokers, in Red` `abline` `(a = 1.278, b = 0.498,` ` ` `col = ` `"red"` `, lwd = 3)` |

**Output:**

## R

`# Ask for that model summary again` `summary` `(model1)` ` ` `# Fit the model that does ` `# NOT include INTERACTION` `model2 <- ` `lm` `(LungCap ~ Age + Smoke)` `summary` `(model2)` |

**Output:**

>summary(model1) Call: lm(formula = LungCap ~ Age + Smoke + Age:Smoke) Residuals: Min 1Q Median 3Q Max -4.8586 -1.0174 -0.0251 1.0004 4.1996 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.05157 0.18706 5.622 2.7e-08 *** Age 0.55823 0.01473 37.885 < 2e-16 *** Smokeyes 0.22601 1.00755 0.224 0.823 Age:Smokeyes -0.05970 0.06759 -0.883 0.377 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.515 on 721 degrees of freedom Multiple R-squared: 0.6776, Adjusted R-squared: 0.6763 F-statistic: 505.1 on 3 and 721 DF, p-value: < 2.2e-16 > summary(model2) Call: lm(formula = LungCap ~ Age + Smoke) Residuals: Min 1Q Median 3Q Max -4.8559 -1.0289 -0.0363 1.0083 4.1995 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.08572 0.18299 5.933 4.61e-09 *** Age 0.55540 0.01438 38.628 < 2e-16 *** Smokeyes -0.64859 0.18676 -3.473 0.000546 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 1.514 on 722 degrees of freedom Multiple R-squared: 0.6773, Adjusted R-squared: 0.6764 F-statistic: 757.5 on 2 and 722 DF, p-value: < 2.2e-16

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