Quick Regression Demo

1 Call Libraries

Code

library(pander) # nice tables

library(rgl) # 3D graphics

library(car) # companion to applied regression

2 Data

These are the famous Iris data, collected by Edgar Anderson.

Code

data("iris") # iris data

pander(head(iris)) # nice table of top of data

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

3 Irises

4 Regression

Code

fit1 <- lm(Petal.Length ~ Sepal.Length + Sepal.Width, data = iris)

pander(fit1) # nice table

Fitting linear model: Petal.Length ~ Sepal.Length + Sepal.Width
	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	-2.525	0.5634	-4.481	1.484e-05
Sepal.Length	1.776	0.06441	27.57	5.848e-60
Sepal.Width	-1.339	0.1224	-10.94	9.429e-21

5 Visualization

Code

options(rgl.useNULL = TRUE) # suppress separate RGL window.

car::scatter3d(x = iris$Sepal.Length,
               y = iris$Petal.Length,
               z = iris$Sepal.Width,
               xlab = "Sepal Length",
               ylab = "Petal Length",
               zlab = "Sepal Width")

rglwidget()