Multiple Regresi Linear

Untuk menjalankan banyak regresi linear, kita akan menggunakan kembali fungsi Im(). Kita akan memprediksikan harga rumah dari varabel “medv” sebagai variable response, yang berdasar pada variable explanatory “crim”, “zn”, “indus”, “rm”, “age”, ’dis”, “tax”, dan “rad”.

library(rgl)
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(scatterplot3d)
library(FactoClass)

## Loading required package: ade4

## Loading required package: ggrepel

## Loading required package: xtable

library(MASS)

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

Boston2 <- Boston[1:50,]

fit3 <- lm(medv ~ rm +age, data=Boston2)
sp <- scatterplot3d::scatterplot3d(Boston2$rm,
Boston2$age,
Boston2$medv,
angle = 45)
sp$plane3d(fit3, lty.box = "solid")#,
# polygon_args = list(col = rgb(.1, .2, .7, .5)) # Fill color
orig <- sp$xyz.convert(Boston2$rm,
Boston2$age,
Boston2$medv)
plane <- sp$xyz.convert(Boston2$rm,
Boston2$age, fitted(fit3))
i.negpos <- 1 + (resid(fit3) > 0)
segments(orig$x, orig$y, plane$x, plane$y,
col = c("blue", "red")[i.negpos],
lty = 1) # (2:1)[i.negpos]
sp <- FactoClass::addgrids3d(Boston2$rm,
Boston2$age,
Boston2$medv,
angle = 45,
grid = c("xy", "xz", "yz"))