In jr-packages/jrPred: Predictive Analytics 1 day by Jumping Rivers

Course R package

Installing the course R package is straightforward. First install drat, a package that makes it easy to host and distribute packages.

install.packages("drat")

\noindent Then

drat::addRepo("jr-packages")
install.packages("jrPred")

\noindent This R package contains copies of the practicals, solutions and data sets that we require. It will also automatically install any packages, that we use during the course. For example, we will need the caret, mlbench, pROC and splines to name a few. To load the course package, use

library("jrPred")

\noindent During this practical we will mainly use the caret package, we should load that package as well

library("caret")

The cars2010 data set

The cars2010 data set contains information about car models in $2010$. The aim is to model the FE variable which is a fuel economy measure based on $13$ predictors. Further information can be found in the help page, help("cars2010", package = "AppliedPredictiveModeling").

The data is part of the AppliedPredictiveModeling package and can be loaded by

data(FuelEconomy, package = "AppliedPredictiveModeling")

\noindent There are a lot of questions below marked out by bullet points. Don't worry if you can't finish them all, the intention is that there is material for different backgrounds and levels

An Initial Model

• Prior to any analysis we should get an idea of the relationships between variables in the data. Use the pairs function to explore the data.

• Create a simple linear model fit of FE against EngDispl using the train function. Hint: use the train function with the lm method.

m1 = train(FE ~ EngDispl, method = "lm", data = cars2010)

• Using your model, what level of FE would you expect a car with an EngDispl of 7 to have?

predict(m1, newdata = data.frame(EngDispl = 7))

• What is the training error rate (RMSE) for this model?

sqrt(mean(resid(m1)^2))
# or 
RMSE(fitted.values(m1), cars2010$FE)

Extending the model

• Fit a model with the linear and quadratic terms for EngDispl and call it m2

m2 = train(FE ~ poly(EngDispl, 2, raw = TRUE), data = cars2010,
    method = "lm")

• How do the two models compare in training error rate? Have we improved the model?

sqrt(mean(resid(m2)^2)) - sqrt(mean(resid(m1)^2))
# Yes

• Add NumCyl as a predictor to the simple linear regression model m1 and call it m3

m3 = train(FE ~ EngDispl + NumCyl, data = cars2010, method = "lm")

• What is the RMSE for m3?

sqrt(mean(resid(m3)^2))

• Does the model improve with the addition of an extra variable?

Visualising the models

• Plot EngDispl against Fe

plot(cars2010$EngDispl, cars2010$FE)

• We can use the abline() function to overfit a model with one predictor that is linear in the x and y axes. Try running

abline(m1$finalModel, col = 2)

• For m2, the model is a quadratic fit. The abline() function only draws straight lines and so is no longer suitable. We'll switch to the lines() function. By overplotting the original points against the a sequence of predicted values we'll get a representation of the fit

x_values = seq(1,8.4,0.1)
new_pred_values = predict(m2, newdata = data.frame(EngDispl = x_values)
lines(x = x_values, y = new_pred_values, col = 3)

• Does this back up the RMSE results from the previous question?

# Yes, line looks to curve with the data now we have added a quadratic term

• It's harder to compare the visually compare m3 with m2 and m1 as we now have more than 1 predictor. The jrPred package contains a plot3d function to help with viewing these surfaces in 3D.

## points = TRUE to also show the points
plot3d(m3, cars2010$EngDispl, cars2010$NumCyl, cars2010$FE,
    points = FALSE)

\noindent We can also examine just the data interactively, via

threejs::scatterplot3js(cars2010$EngDispl, cars2010$NumCyl,
    cars2010$FE, size = 0.5)

jr-packages/jrPred documentation built on May 6, 2019, 7:17 a.m.