When you use R, you are using a program that is constantly evolving. The user-contributed packages are also evolving as well. Normally this is not that big a deal. But sometimes it is. Continue reading
Category Archives: Statistics
Why secondary data analysis takes a lot longer
Someone posted a question noting that most of the statistical consulting projects that they worked on finished in a reasonable time frame, a few were outliers. They took a lot longer and required a lot more effort by the statisticians. Were there any common features to these outliers they wondered. So they asked if anyone else had identified methodological features of projects that went overtime. I only had a subjective impression, but thought it was still worth sharing. Continue reading
PMean: About those “awful” election predictions
If you were on Mars for the past few days, you may not have noticed that Donald Trump has won the election. There has been a lot of commentary lately about how badly the predictions about the U.S. election have been and someone mentioned that even Nate Silver at the fivethirtyeight website had a predicted probability of a Clinton win at 71%. I wrote a brief comment that predicting an event with 71% probability does not mean that your prediction was “wrong” if the other event occurs. Continue reading
PMean: A simple example of pipes in R
At the Joint Statistical Meetings this year, I learned a lot about recent developments in R, and not so recent developments that I was totally clueless about. One of those developments was the use of pipes in R. I wanted to show a simple example of how pipes can simplify your code. Continue reading
PMean: Small group presentations using screen sharing tools
I received a suggestion for the Kansas City R Users Group to use screen sharing tools. I am going to experiment with this a bit. Here are two tools worth trying. Continue reading
PMean: Misunderstanding autism
A friend of mine posted an inspiring story published in the Washington Post. Unfortunately, it did not inspire me, but rather made me worried about how often we misunderstand autism and how much trouble this causes. It’s not statistics, per se, but rather represents an example of how research on new approaches for patients with autism can end up being abusive. Continue reading
PMean: Measuring pixels in an R graph
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates unit conversions. If you want to measure the length of a diagonal line segment in an R graph, you need to calculate the size of the plotting region in pixels, compare that to the range of the plotting region in the x and y directions, and then apply the Pythagorean Theorem. Continue reading
PMean: Rotating text in an R graph
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to rotate text to match a diagonal line in an R graph. It’s trickier than it seems. Continue reading
PMean: Drawing the perfect circle
I have an R cheat sheet, How Big Is Your Graph, that explains how to measure the size of various features of your graph in R. This blog post illustrates how you can use some of the commands described in that cheat sheet to draw a perfect circle. Continue reading
PMean: Independent consulting and the cold call
There’s been some more discussion about getting started as an independent statistical consultant. One person is ready to hang their shingle and proposes to “find a niche I can serve, contact companies in that niche, etc.” but didn’t know what that niche might be. I had one cautionary comment and then discussed finding your niche. Continue reading