Many ways to do the same thing: linear regression

April 7, 2019
By

(This article was first published on R – Statistical Odds & Ends, and kindly contributed to R-bloggers)

One feature of R (could be positive, could be negative) is that there are many ways to do the same thing. In this post, I list out the different ways we can get certain results from a linear regression model. Feel free to comment if you know more ways other than those listed!

In what follows, we will use the linear regression object lmfit:

data(mtcars)
lmfit <- lm(mpg ~ hp + cyl, data = mtcars)

Extracting coefficients of the linear model

# print the lm object to screen
lmfit

# part of the summary output
summary(lmfit)

# extract from summary output
summary(lmfit)$coefficients[, 1]

# use the coef function
coef(lmfit)

# extract using list syntax
lmfit$coefficients

Getting fitted values for the training data set

# use the predict function
predict(lmfit)

# extract from lm object
lmfit$fitted.values

Getting residuals for the training data set

# use the predict function
resid(lmfit)

# extract from lm object
lmfit$residuals

# extract from lm summary
summary(lmfit)$residuals

To leave a comment for the author, please follow the link and comment on their blog: R – Statistical Odds & Ends.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers

Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)