I see. This is a pretty radical change to how GLM’s would be specified. I think the only realistic way you could make any progress on such a radical proposal is to undertake this change as a project on your own and then give people a demo of a system you’ve built that’s noticeably better than what they’re used to having in R.
— John On Aug 31, 2014, at 1:02 PM, Bradley Setzler <[email protected]> wrote: > Sorry, I meant for those to be in the ... term. > > Let me write them explicitly for the case of 3 independent variables, X1 X2 > X3, seriesRank=2 would be, > > (intercept) > X1.^2 > X2.^2 > X3.^2 > X1.*X2 > X1.*X3 > X2.*X3 > X1.*X2.*X3 > > Bradley > > On Sunday, August 31, 2014 2:55:22 PM UTC-5, John Myles White wrote: > Bradley, you’re forgetting about interactions terms. > > — John > > On Aug 31, 2014, at 12:53 PM, Bradley Setzler <[email protected]> wrote: > >> No problem. >> >> Honestly, I'm not sure formula is a useful way to think about regression, >> the formula is uniquely determined from: >> (depVar, indepVars, data, family, link) >> >> so that the + symbols are redundant given family and link, >> glm(Y ~ X1 + X2 + X3 + X4 + X5 +...., family, link) >> >> and it would be nice to have an explicit intercept argument like, >> glm(Y,X,data,family,link,intercept=true) >> >> Adding to the wish list, I would like to see something like a series option >> for non-parametric regression, >> glm(Y,X,data,family,link,seriesRank=2) >> where seriesRank=2 means all of the terms X1.^2, X1.*X2, X1.*X3,...,X5.^2 >> are included as regressors. >> >> Bradley >> >> >> >> >> On Sunday, August 31, 2014 2:32:30 PM UTC-5, John Myles White wrote: >> Merged. Thanks, Bradley. >> >> — John >> >> On Aug 31, 2014, at 12:29 PM, Bradley Setzler <[email protected]> wrote: >> >>> Thank you for suggesting this, John. >>> >>> https://github.com/JuliaStats/GLM.jl/pull/90 >>> >>> Bradley >>> >>> >>> On Sunday, August 31, 2014 1:33:04 PM UTC-5, John Myles White wrote: >>> Bradley, it’s especially easy to edit documentation because you can make a >>> Pull Request right from the website. >>> >>> — John >>> >>> On Aug 31, 2014, at 11:30 AM, Bradley Setzler <[email protected]> wrote: >>> >>>> Thank you Adam, this works. >>>> >>>> Let me suggest that this information be included in the GLM documentation: >>>> >>>> To fit a GLM model, use the function, >>>> glm(formula, data, family, link), >>>> where, >>>> - formula uses column symbols from the DataFrame data, e.g., if >>>> names(data)=[:Y,:X], then a valid formula is Y~X; >>>> - data is a DataFrame which may contain NA values, the rows with NA values >>>> will be ignored (apparently); >>>> - family may be chosen from Binomial(), Gamma(), Normal(), or Poisson(), >>>> and the parentheses are required; and, >>>> - link may be chosen from the list in the GLM documentation, such as >>>> LogitLink(), and again the parentheses are required. For some families, a >>>> default link is available so the link argument may be left blank. >>>> >>>> Bradley >>>> >>>> >>>> On Sunday, August 31, 2014 12:56:19 PM UTC-5, Adam Kapor wrote: >>>> This works for me: >>>> >>>> ``` >>>> julia> fit(GeneralizedLinearModel,Y~X,data,Binomial(),ProbitLink()) >>>> >>>> DataFrameRegressionModel{GeneralizedLinearModel,Float64}: >>>> >>>> Coefficients: >>>> >>>> Estimate Std.Error z value Pr(>|z|) >>>> >>>> (Intercept) 0.430727 1.98019 0.217518 0.8278 >>>> >>>> X 2.37745e-17 0.91665 2.59362e-17 1.0000 >>>> >>>> julia> fit(GeneralizedLinearModel,Y~X,data,Binomial(),LogitLink()) >>>> >>>> DataFrameRegressionModel{GeneralizedLinearModel,Float64}: >>>> >>>> Coefficients: >>>> >>>> Estimate Std.Error z value Pr(>|z|) >>>> >>>> (Intercept) 0.693147 3.24037 0.21391 0.8306 >>>> >>>> X -7.44332e-17 1.5 -4.96221e-17 1.0000 >>>> >>>> ``` >>>> >>>> >>>> On Sunday, August 31, 2014 1:27:15 PM UTC-4, Bradley Setzler wrote: >>>> Has anyone successfully performed probit or logit regression in Julia? The >>>> GLM documentation does not provide a generalizable example of how to use >>>> glm(). It gives a Poisson example without any suggestion of how to switch >>>> from Poisson to some other type. >>>> >>>> Using the Poisson example from GLM documentation works: >>>> >>>> julia> X = [1;2;3.] >>>> julia> Y = [1;0;1.] >>>> julia> data = DataFrame(X=X,Y=Y) >>>> julia> fit(GeneralizedLinearModel, Y ~ X,data, Poisson()) >>>> DataFrameRegressionModel{GeneralizedLinearModel,Float64}: >>>> Coefficients: >>>> Estimate Std.Error z value Pr(>|z|) >>>> (Intercept) -0.405465 1.87034 -0.216787 0.8284 >>>> X -3.91448e-17 0.8658 -4.52123e-17 1.0000 >>>> >>>> But does not generalize: >>>> >>>> julia> fit(GeneralizedLinearModel, Y ~ X ,data, Logit()) >>>> ERROR: Logit not defined >>>> >>>> julia> fit(GeneralizedLinearModel, Y ~ X, data, link=:ProbitLink) >>>> ERROR: `fit` has no method matching fit(::Type{GeneralizedLinearModel}, >>>> ::Array{Float64,2}, ::Array{Float64,1}) >>>> >>>> julia> fit(GeneralizedLinearModel, Y ~ X, data, >>>> family="binomial",link="probit") >>>> ERROR: `fit` has no method matching fit(::Type{GeneralizedLinearModel}, >>>> ::Array{Float64,2}, ::Array{Float64,1}) >>>> >>>> ....and a dozen other similar attempts fail. >>>> >>>> >>>> Thanks, >>>> Bradley >>>> >>> >> >
