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Currently you have to fit single models one at a time.
The text was updated successfully, but these errors were encountered:
This is probably achievable with purrr
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Actually, a much nicer way (and cleaner dependency way) would be to switch to data.table as the backend.
data.table
> dt <- data.table('x'=rnorm(100),'y'=rnorm(100),'g'=sample(c('a','b'),100,replace=T),key='g') > GLMs <- dt[,list(glms=list(glm(formula=y~x))), by='g'] > GLMs[g == "a", glms][[1]] Call: glm(formula = y ~ x) Coefficients: (Intercept) x 0.02669 0.16465 Degrees of Freedom: 46 Total (i.e. Null); 45 Residual Null Deviance: 49.08 Residual Deviance: 48.1 AIC: 140.5
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Currently you have to fit single models one at a time.
The text was updated successfully, but these errors were encountered: