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] > <javascript:>> 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 <https://github.com/JuliaStats/GLM.jl> 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 >>>> >>>> >> >
