Emil O W Kirkegaard

What sort of R^2 do you get with all the n-grams? also, could use 'p.adjust' to do non-Bonferroni multiple correction.

Like in multiple regression? Currently have about 280 ngrams and ~1900 names, so could use OLS MR and get CV R2.

The p_cor is the corrected p value. glmnet not suitable for categorical predictors. Need a good function for LASSO with GLM.

Huh. Quite good R2 even with OLS. R2=50%, R2-CV=29%. (10 fold CV, 5 runs)

That *is* surprising. Do even surnames, marking family history/class/genetics, achieve CV performance like 29%?

I guess there's some sort of very strong name-preferences->parental quality going on there. Wonder what they're preferring.

From my skimming, seems a lot of it is Muslim related. They tend to use a's a lot. The d value for "a" anywhere is -.50.

Hah! But how much of population R^2 does Muslim country descent explain in other datasets? Sets a lower bound then.