Recent content by Jake

I've recommended that to a person or two in the past. Although the app linked in this thread deals with binary predictors, my PANGEA app supports categorical variables with up to like 12 levels or something, as well as arbitrary contrasts. So my thinking has been that if you specify a 12-level...

What wretched soul has summoned me to the board? Yes, all my mixed model power apps are for all categorical predictors only. Not only that, but they are for balanced, orthogonal designs. Those are the only special cases in which the math works out simply enough that you can get easy analytic...

I'm telling you to use math. Hint: logarithms You see there's this rule here about having to show effort toward solving your question... ;)

The series is 3 * 2^n for n = 0, 1, 2, ... To find the value of n that yields 3M, set the expression above equal to 3M and solve for n.

Sounds good, gotta try that

In my opinion these residual plots look fine enough, I wouldn't be too worried.

Another, similar way to approach this would be partial least squares regression.

If (and only if) you're working with a dictionary where you know that every value is a list, then you could cast the dict values (discarding the keys) into a list of lists using `list(x.values())` and then apply one of the recipes here for flattening a list...

First of all, that's a dictionary, not a list. You construct dictionaries with curly braces and lists with square brackets. A dictionary maps keys to values. A dictionary's .keys() method will return, well, its keys: in this case the list names ['Titles', 'Entities']. The .values() method will...

Like others already mentioned, this is a crossed random effects model, which can easily be fit in most (but not all) stats packages, including lme4 in R, SAS PROC MIXED/GLIMMIX, and others. The syntax is package-specific of course but usually it's as simple as just add separate random effect...

"Unobserved heterogeneity" in logistic regression is nothing to be afraid of. I address this here, arguing directly against Allison and Mood: http://jakewestfall.org/blog/index.php/2018/03/12/logistic-regression-is-not-fucked/ Overdispersion is a completely different issue. In logistic...

Yes, I agree. Mainly I think the alternative method is interesting as a way of understanding what ANCOVA is doing "under the hood." But in practice you wouldn't normally literally do it that way.

This is almost true, but not quite. You're missing one step here: you also need to regress the independent variable (IV) on the covariate and save those residuals too. Then if you regress the DV residuals (which you already mentioned) on the IV residuals (which I just mentioned), the resulting...

In your own example, r = 0.53 and R^2 = 0.28....so clearly they're not the same.

Feel free to explain the methodological problems you spotted.