Visualizing Multiple Linear Regression in 3D with R
We are going to create our own dataset to use for the multiple regression by using vectors concept in R and create a graph. The data we are going to use has two independent variables – age and experience and the dependent variable is income. Here the procedure same as the above codes.
Loading Packages
R
#loading the package rgllibrary(rgl) |
Building Linear Regression Model
R
#data to be used in the plot age <- c(25,30,47,32,43,51,28,33,37,39,29,47,54,51,44,41,58,23,44,37)exp <- c(1,3,2,5,10,7,5,4,5,8,1,9,5,4,12,6,17,1,9,10)income <- c(30450,35670,31580,40130,47830,41630,41340,37650,40250,45150,27840,46110, 36720,34800,5130,38900,63600,30870,44190,48700)#multiple linear regression modellm_model= lm(income ~ age + exp)print(lm_model) |
Output:
Call:
lm(formula = income ~ age + exp)
Coefficients:
(Intercept) age exp
28982.46 68.95 1082.37
Visualizing Regression Graph
R
#create new window for 3d plottingopen3d()#plotting the model in the created windowplot3d(lm_model, plane.col='blue')#title of the graphtitle3d("Multiple Linear Regression 3D Plot")# add animation for 30 sec durationplay3d(spin3d(axis = c(0, 0, 1)), duration = 30) |
Output:
3D Multiple Regression Graph with rgl package in R
The output shown here helps us to predict the income when the experience and income is given. We can also observe that the dataset had an outlier, which may cause the model performance to decrease.
The output in R-studio looks like this:
Output in R studio
3D Multiple Regression Graph with rgl package in R
In this article, we are going to walk through the process of creating a 3D multiple regression graph using the rgl package in R programming language in detail.
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