Last Updated on August 15, 2020
In this post you will discover 4 recipes for linear regression for the R platform.
You can copy and paste the recipes in this post to make a jump-start on your own problem or to learn and practice with linear regression in R.
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Ordinary Least Squares Regression
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Each example in this post uses the longley dataset provided in the datasets package that comes with R. The longley dataset describes 7 economic variables observed from 1947 to 1962 used to predict the number of people employed yearly.
Ordinary Least Squares Regression
Ordinary Least Squares (OLS) regression is a linear model that seeks to find a set of coefficients for a line/hyper-plane that minimise the sum of the squared errors.
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# load data data(longley) # fit model fit <- lm(Employed~., longley) # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |
Learn more about the lm function and the stats package.
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Stepwize Linear Regression
Stepwise Linear Regression is a method that makes use of linear regression to discover which subset of attributes in the dataset result in the best performing model. It is step-wise because each iteration of the method makes a change to the set of attributes and creates a model to evaluate the performance of the set.
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# load data data(longley) # fit model base <- lm(Employed~., longley) # summarize the fit summary(base) # perform step-wise feature selection fit <- step(base) # summarize the selected model summary(fit) # make predictions predictions <- predict(fit, longley) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |
Learn more about the step function and the stats package.
Principal Component Regression
Principal Component Regression (PCR) creates a linear regression model using the outputs of a Principal Component Analysis (PCA) to estimate the coefficients of the model. PCR is useful when the data has highly correlated predictors.
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# load the package library(pls) # load data data(longley) # fit model fit <- pcr(Employed~., data=longley, validation="CV") # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley, ncomp=6) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |
Learn more about the pcr function and the pls package.
Partial Least Squares Regression
Partial Least Squares (PLS) Regression creates a linear model of the data in a transformed projection of problem space. Like PCR, PLS is appropriate for data with highly-correlated predictors.
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# load the package library(pls) # load data data(longley) # fit model fit <- plsr(Employed~., data=longley, validation="CV") # summarize the fit summary(fit) # make predictions predictions <- predict(fit, longley, ncomp=6) # summarize accuracy mse <- mean((longley$Employed - predictions)^2) print(mse) |
Learn more about the plsr function and the pls package.
Summary
In this post you discovered 4 recipes for creating linear regression models in R and making predictions using those models.
Chapter 6 of Applied Predictive Modeling by Kuhn and Johnson provides an excellent introduction to linear regression with R for beginners. Practical Regression and Anova using R (PDF) by Faraway provides a more in-depth treatment.
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Is there any reason why SLR is not performing better than OLSR? again, the question comes how to judge ‘better’. I notice that rmse(SLR)>rmse(OLSR). If we are aiming for a better fit shouldn’t the rmse decrease?
I think the rmse calculation step missed taking the square root.
rmse <- sqrt( mean((longley$Employed – predictions)^2) )
Yes thank you, this was something I noticed as well and was unsure of.
Jason calculated mse not rmse. So there is no square root.
Anyone , please confirm/oppose my understanding. Are following attributes found best by “Stepwize Linear Regression” algorithm?
GNP + Unemployed + Armed.Forces + Year
We cannot know what algorithm will be best for your data, try a suite of methods and compare the skill of each.
See this post:
https://machinelearningmastery.com/a-data-driven-approach-to-machine-learning/
Sorry, I was not clear. I am not asking which algorithm is best. I am simply running the example code above of ““Stepwize Linear Regression” . Then I am trying to understand the what gets printed on screen. (Thus the data is “longley”. )
I see that three steps/iterations are performed.
My understanding:
1. After the first iteration “GNP.deflator” is discarded because its AIC was lowest(-35.163)
2. After the second iteration “Population” is discarded because its AIC was lowest(-36.799)
3. After 3rd step GNP should be discarded. ( Its AIC is -31.879)
Is my understanding correct?
Ah, I see. The result is the variables suggested or chosen for the final model.