Multiple linear regression

Import data

house <- read.csv('https://github.com/nchelaru/practical_statistics/raw/master/kc_house_data.csv')
house$date <- as.Date(house$date, "%Y%m%dT000000")
head(house)
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multiple_linear_regression (R)
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Multiple regression

The jtools package is a very useful one for summarizing and visualizing regression results. For more information, see here.

library(jtools)
house_lm <- lm(price ~ ., 
               data=house, 
               na.action=na.omit)
summ(house_lm)
multiple_linear_regression (R)
R

Stepwise regression

library(MASS)
step <- stepAIC(house_lm, direction="both")
summ(step)
multiple_linear_regression (R)
R

Weighted regression

library(lubridate)
house$Year = year(house$date)
house$Weight = house$Year - 2005
house_wt <- lm(price ~ ., 
               data=house, 
               weight=Weight)
summ(house_wt)
multiple_linear_regression (R)
R

Compare models

plot_coefs(house_lm, house_wt, step, 
           scale = TRUE, 
           model.names = c("Full", "Weighted", "Stepwise"))
multiple_linear_regression (R)
R

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