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
>
>
