wip: recipes for regression models. should close #290 #293

mkborregaard · 2020-01-30T13:26:39Z

Here's a basic example usage:

using StatsPlots, GLM, RDatasets
iris = dataset("datasets", "iris")
mod = lm(@formula(PetalLength ~ PetalWidth), iris)
plot(mod)
@df iris scatter!(:PetalWidth, :PetalLength, ms = 2, c = :black, legend = :topleft)

This is really rough and does only address the bivariate case - we're waiting for a general interface to StatsModels (will open an issue there)
cc @Tokazama

daschw · 2020-01-31T08:15:13Z

Nice.

Tokazama · 2020-01-30T14:04:05Z

src/statsmodels.jl

@@ -0,0 +1,8 @@
+# Note both predict and RegressionModel are defined in StatsBase
+@recipe function f(mod::RegressionModel)
+    newx = [ones(200) range(extrema(mod.model.pp.X[:,2])..., length = 200)]


Can we be sure that the second column is what we want to plot?

Maybe this is a good hack for now?

@recipe function f(mod::RegressionModel, name) newx = [ones(200) range(extrema(mod.model.pp.X[:,findfirst(==(name), coefnames(mod))])..., length = 200)] ....

So the interface being name can be a symbol, but also a Tuple or Vector of symbols, length, and then the plot being bivariate or 3d depending on that, and the regression keeping all the other input variables at their mean?

I just meant that the second column isn't always going to be what we want to plot.

For example...

mod = lm(@formula(PetalLength ~ 0 + PetalWidth), iris)

would get rid of the intercept so the 2nd column wouldn't even exist. I was just trying to find something that would ensure the right coefficient is grabbed from the model matrix.

Yes I know and I agree, I was just trying to see if we could make it even more general

That would actually be really convenient to be able to add multiple coefficients and see how they are interacting visually! I think this would also make for a good long term user interface when the stats ecosystem will adopt a better way of extracting information from models using coefficient names.

mod.model.pp.X is specific to GLM, you should rather use modelmatrix(mod) instead.

Right now only works for the bivariate case

ffevotte · 2020-04-17T22:36:41Z

(X-ref : discourse)

Thanks for this feature!

I have been able to successfully use it on simple models. However, it still fails in more complex examples, such as the following:

using DataFrames
data = DataFrame(x = rand(100));
data.y = 1 .+ 2*data.x .+ rand(100);

using GLM
model = lm(@formula(y ~ x + x^2), data)  # @formula(y ~ x) works well

using Plots; gr()
using StatsPlots
plot(xlabel="x", ylabel="y", legend=:bottomright)
plot!(data.x, data.y, label="data", seriestype=:scatter)
plot!(model)

for which I get the following error / stack trace:

julia> plot!(model)
ERROR: DimensionMismatch("second dimension of A, 2, does not match length of x, 3")
Stacktrace:
 [1] gemv!(::Array{Float64,1}, ::Char, ::Array{Float64,2}, ::Array{Float64,1}, ::Bo
ol, ::Bool) at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.
4/LinearAlgebra/src/matmul.jl:456                                                 
 [2] mul! at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.4/
LinearAlgebra/src/matmul.jl:66 [inlined]                                          
 [3] mul! at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.4/
LinearAlgebra/src/matmul.jl:208 [inlined]                                         
 [4] * at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.4/Lin
earAlgebra/src/matmul.jl:47 [inlined]                                             
 [5] predict(::LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,L
inearAlgebra.Cholesky{Float64,Array{Float64,2}}}}, ::Array{Float64,2}; interval::Sy
mbol, level::Float64) at /home/francois/.julia/packages/GLM/6V3fS/src/lm.jl:201   
 [6] #predict#79 at /home/francois/.julia/packages/StatsModels/pMxlJ/src/statsmodel
.jl:125 [inlined]                                                                 
 [7] macro expansion at /home/francois/.julia/packages/StatsPlots/0rYaj/src/statsmo
dels.jl:4 [inlined]                                                               
 [8] apply_recipe(::Dict{Symbol,Any}, ::StatsModels.TableRegressionModel{LinearMode
l{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Flo
at64,Array{Float64,2}}}},Array{Float64,2}}) at /home/francois/.julia/packages/Recip
esBase/G4s6f/src/RecipesBase.jl:279                                               
 [9] _process_userrecipes(::Plots.Plot{Plots.GRBackend}, ::Dict{Symbol,Any}, ::Tupl
e{StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.Den
sePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64
,2}}}) at /home/francois/.julia/packages/Plots/cc8wh/src/pipeline.jl:85           
 [10] _plot!(::Plots.Plot{Plots.GRBackend}, ::Dict{Symbol,Any}, ::Tuple{StatsModels
.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Fl
oat64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}}) at /ho
me/francois/.julia/packages/Plots/cc8wh/src/plot.jl:178                           
 [11] #plot!#138 at /home/francois/.julia/packages/Plots/cc8wh/src/plot.jl:158 [inl
ined]                                                                             
 [12] plot!(::Plots.Plot{Plots.GRBackend}, ::StatsModels.TableRegressionModel{Linea
rModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesk
y{Float64,Array{Float64,2}}}},Array{Float64,2}}) at /home/francois/.julia/packages/
Plots/cc8wh/src/plot.jl:155                                                       
 [13] plot!(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64
,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},A
rray{Float64,2}}; kw::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tupl
e{}}}) at /home/francois/.julia/packages/Plots/cc8wh/src/plot.jl:150              
 [14] plot!(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64
,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},A
rray{Float64,2}}) at /home/francois/.julia/packages/Plots/cc8wh/src/plot.jl:144   
 [15] top-level scope at REPL[32]:1

Thanks anyway for StatsPlots, and please don't hesitate to tell me if I can help!

hafez-ahmad · 2020-08-15T09:31:08Z

I am getting error
plot!(model)
ERROR: Cannot convert StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}} to series data for plotting
Stacktrace:
[1] error(::String) at .\error.jl:33
[2] _prepare_series_data(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}) at C:\Users\hafez.julia\packages\RecipesPipeline\5RD7m\src\series.jl:8
[3] _series_data_vector(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}, ::Dict{Symbol,Any}) at C:\Users\hafez.julia\packages\RecipesPipeline\5RD7m\src\series.jl:27
[4] macro expansion at C:\Users\hafez.julia\packages\RecipesPipeline\5RD7m\src\series.jl:139 [inlined]
[5] apply_recipe(::Dict{Symbol,Any}, ::Type{RecipesPipeline.SliceIt}, ::Nothing, ::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}, ::Nothing) at C:\Users\hafez.julia\packages\RecipesBase\AN696\src\RecipesBase.jl:282
[6] _process_userrecipes!(::Plots.Plot{Plots.GRBackend}, ::Dict{Symbol,Any}, ::Tuple{StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}}) at C:\Users\hafez.julia\packages\RecipesPipeline\5RD7m\src\user_recipe.jl:35
[7] recipe_pipeline!(::Plots.Plot{Plots.GRBackend}, ::Dict{Symbol,Any}, ::Tuple{StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}}) at C:\Users\hafez.julia\packages\RecipesPipeline\5RD7m\src\RecipesPipeline.jl:68
[8] _plot!(::Plots.Plot{Plots.GRBackend}, ::Dict{Symbol,Any}, ::Tuple{StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}}) at C:\Users\hafez.julia\packages\Plots\ViMfq\src\plot.jl:167
[9] #plot!#127 at C:\Users\hafez.julia\packages\Plots\ViMfq\src\plot.jl:158 [inlined]
[10] plot!(::Plots.Plot{Plots.GRBackend}, ::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}) at C:\Users\hafez.julia\packages\Plots\ViMfq\src\plot.jl:155
[11] plot!(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}; kw::Base.Iterators.Pairs{Union{},Union{},Tuple{},NamedTuple{,Tuple{}}}) at C:\Users\hafez.julia\packages\Plots\ViMfq\src\plot.jl:150
[12] plot!(::StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Array{Float64,1}},GLM.DensePredChol{Float64,LinearAlgebra.Cholesky{Float64,Array{Float64,2}}}},Array{Float64,2}}) at C:\Users\hafez.julia\packages\Plots\ViMfq\src\plot.jl:144
[13] top-level scope at REPL[12]:1
[14] include_string(::Function, ::Module, ::String, ::String) at .\loading.jl:1088

mkborregaard force-pushed the mkb/statsmodels branch from d980c7f to e2bab1b Compare January 30, 2020 20:35

Tokazama reviewed Feb 1, 2020

View reviewed changes

mkborregaard mentioned this pull request Feb 29, 2020

How does one put trendlines with confidence intervals on plots? JuliaPlots/Plots.jl#340

Closed

mkborregaard added 3 commits April 17, 2020 23:11

quick recipe for StatsModels

dc565aa

Right now only works for the bivariate case

Add doc to readme

905a97e

remove mistakenly committed image

7ea5de8

mkborregaard force-pushed the mkb/statsmodels branch from d232496 to 7ea5de8 Compare April 17, 2020 21:11

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wip: recipes for regression models. should close #290 #293

wip: recipes for regression models. should close #290 #293

mkborregaard commented Jan 30, 2020 •

edited

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daschw commented Jan 31, 2020

Tokazama Jan 30, 2020

Tokazama Jan 31, 2020

mkborregaard Feb 2, 2020

Tokazama Feb 2, 2020

mkborregaard Feb 2, 2020

Tokazama Feb 2, 2020

nalimilan Sep 24, 2021

ffevotte commented Apr 17, 2020

hafez-ahmad commented Aug 15, 2020

wip: recipes for regression models. should close #290 #293

Are you sure you want to change the base?

wip: recipes for regression models. should close #290 #293

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mkborregaard commented Jan 30, 2020 •

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