Michael Krabbe Borregaard
Michael Krabbe Borregaard
> I'm assuming you want me to answer those questions as potential design implementations Exactly, I'm leveraging the fact that you have a present interest to pick your brain for...
I was thinking more about using the inbuilt `predict` method, we just need an accessor to get the X values. For the bivariate case ```julia using GLM, StatsModels, StatsPlots, DataFrames,...
Note that I'm having trouble with `ribbon` so I'm using an annoying workaround
oh they can be quick. I think making the statistical objects offer the correct data back with accessors is the right way to go here
I could make a PR based on this to start with and we could hash it out from there? ```julia using RecipesBase @recipe function f(mod::RegressionModel) newx = [ones(200) range(extrema(mod.model.pp.X[:,2])..., length...
Actually this is mostly in place already. We just need to change the behaviour of `density`. Consider ```julia a, b = randn(10000), randn(10000) dens = StatsPlots.KernelDensity.kde((a,b)) plot(dens) ``` ![Skærmbillede 2019-10-02...
Could you provide some data as well, making your mwe runnable?
That's because StatsPlots is an extension to Plots, which always uses 1-row matrices for passing several series. The other syntax is from the DataFrames package (StatsPlots accepts any Table type)....
Yeah, I can see that. We'd just also be hesitant to change that syntax, as it is the central idea of all of the Plots system. I'd say that it's...