How to create an only interaction regression model in R?

Mostly, we start with creating models by including single independent variables effect on the dependent variable and then move on to interaction. But if we are sure that there exists some interaction among variables and we are looking for the interaction effect then only interaction regression model can be created. This can be done by using colon sign between variables to signify the interaction as shown in the below examples.

Example1

Consider the below data frame:

Live Demo

> x1<-rpois(20,1)
> x2<-rpois(20,2)
> x3<-rpois(20,5)
> y<-rpois(20,10)
> df1<-data.frame(x1,x2,x3,y)
> df1

Output

x1 x2 x3 y
1 1 3 10 8
2 0 3 9 11
3 1 1 6 5
4 1 1 2 8
5 3 3 5 5
6 0 2 5 10
7 0 1 4 14
8 0 0 0 8
9 1 4 4 7
10 2 3 5 8
11 2 2 6 5
12 1 0 6 11
13 1 2 7 7
14 0 1 3 7
15 2 2 3 5
16 1 1 6 13
17 3 0 5 6
18 0 2 6 8
19 1 1 2 8
20 0 1 7 10

Creating regression model with only interaction of variables:

Example

> Model1<-lm(y~x1:x2+x1:x3+x2:x3,data=df1)
> summary(Model1)

Output

Call:
lm(formula = y ~ x1:x2 + x1:x3 + x2:x3, data = df1)

Residuals:
Min 1Q Median 3Q Max
-3.2093 -1.2583 -0.6080 0.5682 4.7907

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.19978 0.90379 10.179 2.14e-08 ***
x1:x2 -0.39247 0.30978 -1.267 0.223
x1:x3 -0.14849 0.13996 -1.061 0.304
x2:x3 0.04883 0.06907 0.707 0.490
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.323 on 16 degrees of freedom
Multiple R-squared: 0.3316, Adjusted R-squared: 0.2063
F-statistic: 2.646 on 3 and 16 DF, p-value: 0.08444

Example2

Live Demo

> IV_1<-rnorm(20,5,1)
> IV_2<-rnorm(20,5,0.98)
> IV_3<-rnorm(20,20,3.25)
> D<-rnorm(20,1,0.004)
> df2<-data.frame(IV_1,IV_2,IV_3,D)
> df2

Output

     IV_1    IV_2      IV_3     D
1 3.827016 4.760877 17.64892 0.9960714
2 4.623803 5.152936 21.48162 1.0013076
3 5.783128 5.100011 16.10671 1.0017913
4 5.991171 4.718291 13.44091 1.0047349
5 5.229284 4.712669 22.01410 0.9996455
6 5.336851 5.460088 19.56821 1.0045880
7 4.352768 4.350663 18.58638 1.0003473
8 4.556793 2.853435 18.30430 0.9988200
9 6.161752 5.003778 23.22494 1.0020493
10 5.065051 5.845684 16.47238 1.0011333
11 4.317532 5.960619 23.11946 1.0015382
12 6.634342 4.714110 16.62322 1.0040192
13 4.415151 4.940207 19.55201 0.9988561
14 6.129936 6.481631 19.30220 0.9990506
15 5.581201 4.369263 21.86459 0.9991853
16 5.379293 4.871669 14.22654 1.0042899
17 4.689259 5.475507 19.16673 1.0078011
18 5.886390 4.367721 19.54971 1.0038578
19 3.358135 4.545323 21.81248 1.0041536
20 4.011436 5.555905 23.64763 1.0070755

Example

> Model2<-lm(D~IV_1:IV_2+IV_1:IV_3+IV_2:IV_3+IV_1:IV_2:IV_3,data=df2)
> summary(Model2)

Output

Call:
lm(formula = D ~ IV_1:IV_2 + IV_1:IV_3 + IV_2:IV_3 + IV_1:IV_2:IV_3,
data = df2)

Residuals:
Min 1Q Median 3Q Max
-0.0037361 -0.0021382 -0.0000464 0.0019465 0.0054880

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.516e-01 1.914e-02 49.720 <2e-16 ***
IV_1:IV_2 1.981e-03 7.314e-04 2.709 0.0162 *
IV_1:IV_3 4.910e-04 2.055e-04 2.389 0.0305 *
IV_2:IV_3 5.106e-04 1.925e-04 2.652 0.0181 *
IV_1:IV_2:IV_3 -1.989e-04 7.677e-05 -2.590 0.0205 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.002737 on 15 degrees of freedom
Multiple R-squared: 0.3487, Adjusted R-squared: 0.175
F-statistic: 2.008 on 4 and 15 DF, p-value: 0.1451