The goal of MLRnewbie is to provides functionality for conducting multiple linear regression analysis. The packages contains functions that can carry out model construction, residual calculation, and model sum of squares summary.
There are 3 functios:
* linearRegression()
Generate linear model.
* model_residuals()
Calculate linear model’s residuals, including
studentized residuals.
* ssmodel()
Summarize SSR, SSE, and SSY for the linear model.
## Import the library
library(MLRnewbie)
## Import data set
mc <- mtcars
## Multiple linear regression with intercept
## We choose "cyl", "disp","hp" in mtcars as X, and "mpg" as Y.
model_car <- linearRegression(data = mc,
predictors = c("cyl", "disp","hp"),
response = "mpg")
#> [1] Regression coefficients:
#> Estimate Std_Err t_statistics p_value
#> (Intercept) 34.18491917 2.59077758 13.194849 1.536549e-13
#> cyl -1.22741994 0.79727631 -1.539516 1.349044e-01
#> disp -0.01883809 0.01040369 -1.810711 8.092901e-02
#> hp -0.01467933 0.01465087 -1.001943 3.249519e-01
#>
#> [1] Multiple R squared:
#> [1] 0.7678877
## Calculating useful residuals. From the left to right, they are Residual, Std_Residual, and Studentized_Residual.
head(model_residuals(model_car))
#> Residual Std_Residual Studentized_Residual
#> Mazda RX4 -1.191579 -0.3900089 -0.4074351
#> Mazda RX4 Wag -1.191579 -0.3900089 -0.4074351
#> Datsun 710 -3.075548 -1.0066400 -1.0533824
#> Hornet 4 Drive 1.054553 0.3451598 0.3593604
#> Hornet Sportabout 3.685035 1.2061276 1.2644356
#> Valiant -2.940500 -0.9624383 -0.9940011
## All three sum of squares of the linear regression model
ssmodel(model_car)
#> Sum_of_Square degree_freedom
#> SSR 864.6778 3
#> SSE 261.3694 28
#> SST 1126.0472 31
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