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
