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] > <javascript:>> 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 >>> >>> >
