Merging Data Video Tutorial

Here's a video tutorial where I walk through some code that does what the previous post describes.



The FRED data is used extensively for macroeconomics. You might these data useful for joining in graph fights in the blogosphere.

Common Data Creation Commands

Here is a video tutorial where I go through some of the most commonly used commands in creating and manipulating data. As soon as I want to do more than just running a single regression, I use these commands more than any other set of commands (in some of the other videos, you may have seen these).



Here is the code I use in the video if you would like to try out these commands for yourself.

Computing Statistical Power

Today's task is to compute a statistical power function in R. For a good definition of power, go here. Thinking about the power of a testing procedure is an often-overlooked aspect of hypothesis testing.

I also explain what power (in a statistical sense) is in the video, as well as use my shadenorm() function to show what power looks like on the classical hypothesis testing graph. Along the way, I demonstrate how writing a function can make your life easier. Here's the video:



Here is the script file I walk through in the video (copy into notepad for a better view):



My script file uses a new version of my shadenorm() command that is a little easier to use (only change: now, there are no justbelow or justabove arguments. If you just specify below, that's justbelow=TRUE...).

This is available here:

Video Tutorial on Robust Standard Errors

Update: I have included a modified version of this summaryR() command as part of my package tonymisc, which extends mtable() to report robust standard errors. The tonymisc package is available on CRAN through the install.packages() command.

If you have the right R commands at your disposal, it is simple to correct for heteroskedasticity using the robust correction that is commonly-used among economists. I recorded a video tutorial to describe the simplest (and most flexible) way I know to get R to compute robust standard errors.

The key is to use a "summary-style" command that has an option to correct for heteroskedasticity. The command I like to use is called summaryR(). Here is the script file with the summaryR() command.



I found this function on an R-help discussion board where several people were answering someone's question about extending the summary.lm() command.

I deserve none of the credit for writing this (credit goes to John Fox), but I consider it my duty to point out how nice this function is. I demonstrate how to use the function in this video



Here are is the script file I used in the video:



Here's a link to the data set.

Video Tutorial on IV Regression

Update: [1 May 2011] I am working on a better augmentation of the current IV regression functions (specifically ivreg() in AER) in R. I will post a link here to my new method/function for IV regression when I finish debugging the code.

Update 2: [15 May 2011] The function I wrote here does not work with factors due to the way in which I constructed fitted values for the standard error correction in the second stage.

I recorded a new video tutorial on how to conduct an IV regression. This time I used my new ivregress() command, which has much better syntax than my old one. There are several reasons I needed to do this:

1. Relative to my previous post on IV regression, I have added the ability to conduct overidentifying tests. Now, my ivregress() command has all of the basic functionality of Stata's 2sls option to ivregress.
2. Relative to my previous video tutorial on IV regression, this video uses my newer command, which as much better syntax. As such, I will use this video tutorial to replace the old one.
   
   

Here is the R script file I used to define my ivregress() command and its companion summary command sum.iv().



To get ivregress() and sum.iv() to work, (1) copy the text in the above text box into an R script, (2) run all (or just download and run the R script) and (3) save into your default workspace if you want to use the commands into the future. Then, you're good to go. See my video demonstrating how to use the command here:



Here is the code I used to produce the output in the video.



Finally, here is a link to the synthetic "market share" data I used in the video.

I hope you find this command helpful.

How did I make this plot?

To make this plot, I used R's plot(), points() and lines() commands. If you have been wanting to learn how to plot in R, watch it unfold in this video tutorial:



Also, here's the code I used:

How to Shade Under a Normal Density in R

The easiest-to-find method for shading under a normal density is to use the polygon() command. That link is to the first hit on Google for "Shading Under a Normal Curve in R." It works (like a charm), but it is not the most intuitive way to let users produce plots of normal densities.

In response to the standard polygon() approach, I wrote a function called shadenorm() that will produce a plot of a normal density with whatever area you want shaded. The code is available here (in a newer post; I updated the function slightly).

Copy into an R script and run all of it if you want to use the shadenorm() command. To show you how to use it, I also recorded a video tutorial in R to demonstrate how to use the shadenorm() command.




Here is the code I use in the video:



There are a lot of applications where you may want to produce a plot with a normal density with some region shaded. I hope you find this function useful.

How to Bootstrap in R: The Case of R-squared

I recorded a new video tutorial whose original intent was to demonstrate how to write a for loop. As I wanted to make the for loop count for something, I decided that my application would be to write some code that computes the bootstrap approximation to the sampling distribution. This is a common econometric application.

Here is the video where I describe the code and the method.



As with most of the videos here, I am merely describing the application. You should understand why you want to bootstrap before you consider running this code (unless you just want to see a for loop in action).

Here is the code I used:



I am not going to post the data on this one, but the code should be easy to modify to work with any data set you use.

Video Tutorial on Instrumental Variables in R

Update: I have replaced this video tutorial with a video tutorial on a newer, easier to use IV regression command. Check out that command here.

In this video, I show how to use my instrumental variables function in R, ivreg(), along with its companion summary command, sum.iv().



Here is a link to the synthetic "market share" data I used in the video.

Basic Plots in R

Here's a tutorial I recorded on producing basic plots in R.



I lost the script file I used to create the video to a horrifying black screen of death, but I used the data from the previous post (available here). Hopefully, the video is clear enough that you can follow along with your own window of R.

Testing Multiple Linear Restrictions in R

This video shows a couple of things:

1. How to test (multiple) linear restrictions in R.
2. How to install a package in R.



Here is the code I use in the video (with some extra comments).



Here is the data set that I use in the video.

How Does R Deal with Categorical Explanatory Variables?

Here's a video tutorial where I demonstrate an answer to this question.



This video is designed to instill a baseline level of practical knowledge. There is more to how R treats factors in regression models. An interested reader should Google "R contrasts" for more information.

Here is the code I used in the video:



Here is a link to the data set.

Running a Regression in R

I created another video tutorial on R. This time, I discuss R's lm() command and how to use it for a variety of standard applications.



Here is the code that goes with the video:

Enjoy!

Tutorial on Distributions in R

Here's a video tutorial I put together to go over how to generate a random sample from one of the commonly known parametric distributions in R.



Along the way, I also discuss how some of the properties of estimators are reflected in the computations I perform.

How to read and write Stata data (.dta) files into R

Here's an R tutorial where I explain how to read Stata data files into R (even if you don't own the program Stata). I also offer some other basic tips.



Of note, you can also write Stata .dta files from R (if your coauthors or journals insist on having ".dta" data).

Suppose your R data frame has the name fuzzybunny and you want to save the file to the C: drive as myfuzzydata.dta. After loading the foreign library,

library(foreign)

Here's the syntax:


write.dta(fuzzybunny, "C://myfuzzydata.dta")

Try it out and impress your friends (you know, the friends who only know how to use Stata).

An R Video Tutorial on Entering Data

Here is a video tutorial on entering and manipulating data in R. I plan to do more of these. If you have some suggested topics, let me know in the comments.