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 >>> >> >
