Last Update: February 21, 2022
Multiple linear regression in R can be fitted using stats
package lm
function. Main parameters within lm
function are formula
with y ~ x1 + … + xp
model description and data
with data.frame
object including model variables. Therefore, lm(y ~ x1 + x2, data = model.data)
code line fits model using variables included within model.data
object.
As example, we can fit multiple linear regression of house price explained by its lot size and number of bedrooms using data included within AER
package HousePrices
object [1].
First, we load package AER
for data [2].
In [1]:
library(AER)
Second, we create HousePrices
data object from AER
package using data
function and print first six rows and three columns of data using head
function to view data.frame
structure.
In [2]:
data(HousePrices)
head(HousePrices[,1:3])
Out [2]:
price lotsize bedrooms
1 42000 5850 3
2 38500 4000 2
3 49500 3060 3
4 60500 6650 3
5 61000 6360 2
6 66000 4160 3
Third, we fit model with lm
function using variables within HousePrices
data object, store outcome within mlr
object and print its coefficients estimates. Within lm
function, parameter formula = price ~ lotsize + bedrooms
fits model where house price is explained by its lot size and number of bedrooms.
In [3]:
mlr <- lm(formula = price ~ lotsize + bedrooms, data = HousePrices)
mlr
Out [3]:
Call:
lm(formula = price ~ lotsize + bedrooms, data = HousePrices)
Coefficients:
(Intercept) lotsize bedrooms
5612.600 6.053 10567.352
Courses
My online courses are hosted at Teachable website.
For more details on this concept, you can view my Linear Regression in R Course.
References
[1] Data Description: Sales prices of houses sold in the city of Windsor, Canada, during July, August and September, 1987.
Original Source: Anglin, P., and Gencay, R. (1996). Semiparametric Estimation of a Hedonic Price Function. Journal of Applied Econometrics, 11, 633–648.
[2] AER R Package. Christian Kleiber and Achim Zeileis. (2008). Applied Econometrics with R. Springer-Verlag, New York.