Gaussian Linear

Gaussian linear atau model linear Gaussian adalah sebuah model statistik yang mengasumsikan bahwa hubungan antara sebuah variabel dependen dengan satu atau lebih variabel independen adalah linear dan bahwa kesalahan (error) yang terjadi dalam model dijelaskan oleh distribusi normal (gaussian). Model ini sering juga disebut sebagai model regresi linear Gaussian.

library(matlib)
A <- matrix(c(0, -1, 1, 0, 1, 1, 0, 1, 3, -4, 2, 0, -1, 0, 4, -4), 4, 4)
b <- c(1, 1, 5, -2)
showEqn(A, b)
##  0*x1 + 1*x2 + 3*x3 - 1*x4  =   1 
## -1*x1 + 1*x2 - 4*x3 + 0*x4  =   1 
##  1*x1 + 0*x2 + 2*x3 + 4*x4  =   5 
##  0*x1 + 1*x2 + 0*x3 - 4*x4  =  -2
echelon(A, b, verbose=TRUE, fractions=TRUE)
## 
## Initial matrix:
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  0    1    3   -1    1  
## [2,] -1    1   -4    0    1  
## [3,]  1    0    2    4    5  
## [4,]  0    1    0   -4   -2  
## 
## row: 1 
## 
##  exchange rows 1 and 2 
##      [,1] [,2] [,3] [,4] [,5]
## [1,] -1    1   -4    0    1  
## [2,]  0    1    3   -1    1  
## [3,]  1    0    2    4    5  
## [4,]  0    1    0   -4   -2  
## 
##  multiply row 1 by -1 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1   -1    4    0   -1  
## [2,]  0    1    3   -1    1  
## [3,]  1    0    2    4    5  
## [4,]  0    1    0   -4   -2  
## 
##  subtract row 1 from row 3 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1   -1    4    0   -1  
## [2,]  0    1    3   -1    1  
## [3,]  0    1   -2    4    6  
## [4,]  0    1    0   -4   -2  
## 
## row: 2 
## 
##  multiply row 2 by 1 and add to row 1 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    7   -1    0  
## [2,]  0    1    3   -1    1  
## [3,]  0    1   -2    4    6  
## [4,]  0    1    0   -4   -2  
## 
##  subtract row 2 from row 3 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    7   -1    0  
## [2,]  0    1    3   -1    1  
## [3,]  0    0   -5    5    5  
## [4,]  0    1    0   -4   -2  
## 
##  subtract row 2 from row 4 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    7   -1    0  
## [2,]  0    1    3   -1    1  
## [3,]  0    0   -5    5    5  
## [4,]  0    0   -3   -3   -3  
## 
## row: 3 
## 
##  multiply row 3 by -1/5 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    7   -1    0  
## [2,]  0    1    3   -1    1  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0   -3   -3   -3  
## 
##  multiply row 3 by 7 and subtract from row 1 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    6    7  
## [2,]  0    1    3   -1    1  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0   -3   -3   -3  
## 
##  multiply row 3 by 3 and subtract from row 2 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    6    7  
## [2,]  0    1    0    2    4  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0   -3   -3   -3  
## 
##  multiply row 3 by 3 and add to row 4 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    6    7  
## [2,]  0    1    0    2    4  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0    0   -6   -6  
## 
## row: 4 
## 
##  multiply row 4 by -1/6 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    6    7  
## [2,]  0    1    0    2    4  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0    0    1    1  
## 
##  multiply row 4 by 6 and subtract from row 1 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    0    1  
## [2,]  0    1    0    2    4  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0    0    1    1  
## 
##  multiply row 4 by 2 and subtract from row 2 
##      [,1] [,2] [,3] [,4] [,5]
## [1,]  1    0    0    0    1  
## [2,]  0    1    0    0    2  
## [3,]  0    0    1   -1   -1  
## [4,]  0    0    0    1    1  
## 
##  multiply row 4 by 1 and add to row 3 
##      [,1] [,2] [,3] [,4] [,5]
## [1,] 1    0    0    0    1   
## [2,] 0    1    0    0    2   
## [3,] 0    0    1    0    0   
## [4,] 0    0    0    1    1
Solve(A, b)
## x1        =  1 
##   x2      =  2 
##     x3    =  0 
##       x4  =  1
A <-matrix(c(1,0,1,0,1,0,0,1,0,1,1,0,0,1,0,1),nrow=4,ncol=4)
A
##      [,1] [,2] [,3] [,4]
## [1,]    1    1    0    0
## [2,]    0    0    1    1
## [3,]    1    0    1    0
## [4,]    0    1    0    1
b <-c(475,489,542,422)
Solve(A, b)
## x1     - 1*x4  =   53 
##   x2     + x4  =  422 
##     x3   + x4  =  489 
##             0  =    0

Pengaplikasian gaussian linear dengan dataset mtcars

data(mtcars)
plot(mtcars$wt, mtcars$mpg, xlab = "Weight", ylab = "Miles per Gallon")

Selanjutnya, kita dapat membangun model linear sederhana dengan menggunakan fungsi “lm”.

model <- lm(mpg ~ wt, data = mtcars)

Setelah model dibangun, kita dapat mengevaluasi kualitas model dengan memeriksa koefisien determinasi (R-squared) dan plot residual. Berikut adalah kode untuk menampilkan R-squared dan membuat plot residual:

summary(model)$r.squared
## [1] 0.7528328
plot(model, which = 1)

new_data <- data.frame(wt = 2.5)
predict(model, newdata = new_data)
##        1 
## 23.92395