The R-Help mailing list is a wealth of information. While I have no doubt (well, mostly) that the people who frequent the mailing list are all nice people in real life, woe is the newbie who asks a question that is easily answerable by consulting one of a half-dozen arcane texts, conducting an exhaustive search of list archives, or using R’s internal help system. “Read the posting guide!” will be accompanied by a curt response that often suggests how truly easy it was to find this answer for anyone not still working on their own cell division. (Okay, it’s not really that bad, but it can certainly be an intimidating place due to the sheer number of super-smart residents who have little tolerance for perceived time-wasters.)
This all adds up to my not being quite sure how to take today’s April Fool’s posts by list heavyweight Frank Harrell.
I have never taken a statistics class nor read a statistics text, but I am in dire need of help with a trivial data analysis problem for which I need to write a report in two hours. I have spent 10,000 hours of study in my field of expertise (high frequency noise-making plant biology) but I’ve always thought that statistics is something that can be mastered on short notice.
Briefly, I have an experiment in which a response variable is repeatedly measured at 1-day intervals, except that after a plant becomes sick, it is measured every three days. We forgot to randomize on one of the important variables (soil pH) and we forgot to measure the soil pH. Plants that begin to respond to treatment are harvested and eaten (deep fried if they don’t look so good), but we want to make an inference about long-term responses.
There’s more, including a couple of helpful responses, so you know, read the whole thing. The message ends with this conclusion, which is actually fairly representative of a good number of frantic help-me posts: “I would appreciate receiving a few paragraphs of description of the analysis that I can include in my report, and I would like to receive R code to analyze the data no matter which variables I collect. I do value your time, so you will get my everlasting thanks.”
Take-home message: Read the posting guide, design your analysis carefully, and don’t look crossways at Frank Harrel in a dark alley.