# Why is the square root transformation recommended for count data?

It is often recommended to take the square root when you have count data. (For some examples on CV, see @HarveyMotulsky's answer here, or @whuber's answer here.) On the other hand, when fitting a generalized linear model with a response variable distributed as Poisson, the log is the canonical link. This is sort of like taking a log transformation of your response data (although more accurately it is taking a log transformation of $\lambda$, the parameter that governs the response distribution). Thus, there is some tension between these two.

• How do you reconcile this (apparent) discrepancy?
• Why would the square root be better than the logarithm?
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The square root is approximately variance-stabilizing for the Poisson. There are a number of variations on the square root that improve the properties, such as adding $\frac{3}{8}$ before taking the square root, or the Freeman-Tukey ($\sqrt{X}+\sqrt{X+1}$ - though it's often adjusted for the mean as well).

The square root transformation somewhat improves symmetry - though not as well as the $\frac{2}{3}$ power does [1]:

If you particularly want near-normality (as long as the parameter of the Poisson is not really small) and don't care about/can adjust for heteroscedasticity, try $\frac{2}{3}$ power.

The canonical link is not generally a particularly good transformation for Poisson data; log zero being a particular issue. (It's a good 'transformation' for the population mean of a Poisson in a number of contexts, but not especially of Poisson data.)

As for why people choose one transformation over another (or none) -- that's really a matter of what they're doing it to achieve.

[1]: Plots patterned after Henrik Bengtsson's plots in his handout "Generalized Linear Models and Transformed Residuals" see here (see first slide on p4). I added a little y-jitter and omitted the lines.

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+1, thanks for your help. I gather the square root (or slight variations) is best for normalizing & stabilizing the variance of the Poisson, whereas the log is best for linearizing the mean. Your point about the problem w/ $\log 0$ is also a good one. Nonetheless, I find it counter-intuitive that the best transformation differs between these two contexts. – gung Dec 22 '12 at 17:44
OK, I've been thinking about what you've put here, & here's my synthesis: The optimal transformations differ in these 2 situations b/c what you're trying to achieve differs. The sqrt is better for stabilizing the variance & normalizing the distribution. The log maps the interval $(0, +\infty)$ to $(-\infty, +\infty)$ which allows the transformation of the mean, $\lambda$, to be linear in model parameters. The sqrt does not have this property. W/ a GLiM, it doesn't matter that the variance isn't constant, b/c the response distribution is set as Poisson. Is that about right? – gung Dec 23 '12 at 0:00
What will be linear in the parameters depends on the model. It's perfectly possible for that linearity to be on the original scale or the square root scale or some other scale. Even the - useful/important - 'maps to the real line' property isn't unique to the log function. The reason the log link is 'natural' is because of the way it simplifies the GLM by having a sufficient statistic of $X'y$. – Glen_b Dec 23 '12 at 1:57
+1 The square root is merely a starting point for dealing with count data. The logarithm also is a good choice. The data will often tell you which one is more successful in obtaining a useful and succinct description. Gung, in the answer you refer to, the demonstration that the square root was a good choice lies in the symmetric distribution of the non-outlying residuals apparent in the right hand figure. When you vary the parameters of the simulation, you will find that symmetry is maintained. – whuber Dec 24 '12 at 16:09
@Tomas As for why Freeman-Tukey or $\sqrt{x+3/8}$ rather than $\sqrt{x}$ or $\sqrt{x+c}$ for some other $c$, there are good reasons for both Freeman-Tukey and $\sqrt{x+3/8}$ (for example, to do with making skewness closer to 0), but if you want to get into those in detail, that would be a whole new question. – Glen_b Nov 28 '13 at 22:02