(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

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