Skip to main content

Graphically Comparing Confidence Intervals From Different Models

In a recent paper on Federal Reserve inflation forecast errors (summary blog post, paper) I wanted a way to easily compare the coefficients for a set of covariates (a) estimated from different types of parametric models using (b) matched and non-matched data.

I guess the most basic way to do this would be to have a table of columns showing point estimates and confidence intervals from each estimation model. But making meaningful comparisons with this type of table would be tedious.

What I ended up doing was creating a kind of stacked caterpillar plot. Here it is:

Comparing 95% Confidence Intervals
Comparing 95% Confidence Intervals (Gandrud & Grafström)

I think this plot lets you clearly and quickly compare the confidence intervals estimated from the different models. I didn't include the coefficient point estimates because I was most interested in comparing the ranges. The dots added too much clutter.

I have a link to the full replication code at the end of the post, but these are the basic steps:

  1. I estimated the models using MatchIt and Zelig as per Ho et al. (2007). I created new objects from the results of each model.

  2. I used the confint command to find the 95% confidence intervals.

  3. I did some cleaning up and rearranging of the confidence intervals, mostly using Hadley Wickham's melt function in the reshape package. The basic idea is that to create the plots I needed a data set with columns for the coefficient names, the upper and lower confidence interval bounds, what parametric model the estimates are from, and whether the data set was matched or not. I removed the Intercept and sigma2 estimates for simplicity.

  4. I made the graph using ggplot2. The key aesthetic decisions that I think make it easier to read are: (a) making the lines a bit thicker and (b) making the bands transparent. I liked making the bands transparent and stacking them rather than showing different lines for each set of estimates because this halved the number of lines in the plot. Makes it much crisper.

The full code for replicating this figure is on GitHub Note: this code depends on objects that are created as the result of analyses run using other source code files also on the GitHub site.


Popular posts from this blog

Dropbox & R Data

I'm always looking for ways to download data from the internet into R. Though I prefer to host and access plain-text data sets (CSV is my personal favourite) from GitHub (see my short paper on the topic) sometimes it's convenient to get data stored on Dropbox . There has been a change in the way Dropbox URLs work and I just added some functionality to the repmis R package. So I though that I'ld write a quick post on how to directly download data from Dropbox into R. The download method is different depending on whether or not your plain-text data is in a Dropbox Public folder or not. Dropbox Public Folder Dropbox is trying to do away with its public folders. New users need to actively create a Public folder. Regardless, sometimes you may want to download data from one. It used to be that files in Public folders were accessible through non-secure (http) URLs. It's easy to download these into R, just use the read.table command, where the URL is the file name

Slide: one function for lag/lead variables in data frames, including time-series cross-sectional data

I often want to quickly create a lag or lead variable in an R data frame. Sometimes I also want to create the lag or lead variable for different groups in a data frame, for example, if I want to lag GDP for each country in a data frame. I've found the various R methods for doing this hard to remember and usually need to look at old blog posts . Any time we find ourselves using the same series of codes over and over, it's probably time to put them into a function. So, I added a new command– slide –to the DataCombine R package (v0.1.5). Building on the shift function TszKin Julian posted on his blog , slide allows you to slide a variable up by any time unit to create a lead or down to create a lag. It returns the lag/lead variable to a new column in your data frame. It works with both data that has one observed unit and with time-series cross-sectional data. Note: your data needs to be in ascending time order with equally spaced time increments. For example 1995, 1996

A Link Between topicmodels LDA and LDAvis

Carson Sievert and Kenny Shirley have put together the really nice LDAvis R package. It provides a Shiny-based interactive interface for exploring the output from Latent Dirichlet Allocation topic models. If you've never used it, I highly recommend checking out their XKCD example (this paper also has some nice background). LDAvis doesn't fit topic models, it just visualises the output. As such it is agnostic about what package you use to fit your LDA topic model. They have a useful example of how to use output from the lda package. I wanted to use LDAvis with output from the topicmodels package. It works really nicely with texts preprocessed using the tm package. The trick is extracting the information LDAvis requires from the model and placing it into a specifically structured JSON formatted object. To make the conversion from topicmodels output to LDAvis JSON input easier, I created a linking function called topicmodels_json_ldavis . The full function is below. To