R Part 4a

The total length of the videos in this section is approximately 22 minutes, but you will also spend time running code while completing this section.

You can also view all the videos in this section at the YouTube playlist linked here.

Please download the code file:

Splines

Question 1: In which of the following situations would the use of splines be appropriate?

• You know outcomes for 2 treatments, and you want to predict outcomes for a 3rd treatment for which you have no data {discrete variable}

• You have data for several outcomes on a continuous scale and want to try to predict what the outcome would be for another data point somewhere between your known outcomes {continuous variable}

Regression trees

The code you downloaded has been updated since this video was made, so the code and video don't match at the moment. The reason for the mismatch is that new and improved packages for regression trees come out frequently. 

The video uses an old package. You should still watch the video, because the overall usage is similar.

The code you downloaded shows a package called party. This is my favorite tree package because the labels in the graphics are easy to understand. There is an updated version called partykit that you should try if you have any problems with party. Other popular tree packages include rpart and tree - feel free to try those, too.

Also, the end of video below discusses random forests, which I chose to omit from these modules.

Question 2: Sometimes each node shows a boxplot, and sometimes each node shows a bar plot. Why?

Additions to plots

Question 3: If you don't see something that you've added to your plot, what should you do?

Writing your own functions

Note to all: I thought about excluding the video below, because the example for making a new function refers to "k-means clustering," which I used to include in these modules but recently removed. However, I decided to include the video with this warning not to worry about the details of clustering, so that you have basic information about how to make your own functions.

Question 4: Why might you want to write your own function?

More graphics

Question 5: What is the first thing you should do if you don't know how to use a new function?

Summary of ways to run a model

This is a good moment to show you the notation for running a both linear regression models and trees, even though you may not have seen linear regression in these modules yet. The reason is that R uses similar syntax for various model types, including trees. You can look back on this page as needed as you run models.

Suppose that the data is called d, the outcome variable is Y, and the predictor variables are named X and Z.

The following lines show different ways to run a regression tree or linear regression.

ctree(d$Y~d$X) # works fine

lm(d$Y~d$X)

ctree(Y~X, data=d) # better, and necessary if using the predict function afterwards

lm(Y~X, data=d)

ctree(Y~X+Z, data=d) # multiple predictors

lm(Y~X+Z, data=d)

lm(Y~X+Z+X:Z, data=d) # interaction term (you don't ask for interactions when running a tree, because the idea of interactions is built into the tree algorithm)

lm(Y~X*Z, data=d) # short-cut for including main effects and also interaction

ctree(Y~., data=d) # short-cut for including all columns in d as predictors. Note that you can create a "d" that is a subset of the original data set before using this code, if helpful.

lm(Y~., data=d)

Question 6: If you want to use 10 predictors, should you type out their names with plus signs in between?

And, you're done.

During this tutorial you learned:

• How to graph splines

• To run a regression tree with the party package (or ‘partykit’, ‘rpart’, ‘tree’)

• To plot a regression tree and how to change features of the tree graphic

• How to customize graphics

• How to code your own functions with function() {...return()}

• Multiple ways to define the model you want to run

Functions in review:

spline(), legend(), points(), abline(), text(), function() {...return()}, polygons(), grid(), segments(), box(), axis(), gray(), rainbow(), heat.colors(), colors(), layout(), image()

party function in review:

ctree()