Thanks for your suggestions! So far using GLM and pool!() with a list of categorical variables works fine for me.
On Wednesday, October 1, 2014 5:27:33 PM UTC+3, John Myles White wrote: > > Currently, the way to do this is via the GLM package (or at least its > strategy for generating design matrices), which handles indicators for you. > > Lots of improvements are possible, but we need better categorical data > support at a lower level before we can work on the improvements: > https://github.com/johnmyleswhite/CategoricalData.jl > > — John > > On Oct 1, 2014, at 6:22 AM, Stefan Karpinski <[email protected] > <javascript:>> wrote: > > Probably a better question for julia-stats, but there's also likely some > people here who can answer. > > On Wed, Oct 1, 2014 at 4:23 AM, Andrei <[email protected] <javascript:> > > wrote: > >> Probably simple question, but I can't find any reference. >> >> What is the most convenient way to convert categorical variable to a set >> of dummy vars? >> So far I've been using >> >> int(indicatormat(df[:categoricalvar])) >> >> but it's pretty annoying to do it for every variable, give every >> indicator a name, resolve collinearity, etc. >> Is there any standard way to do these operations? >> > > >
