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Add coefplot and groupedcoefplot for StatisticalModel #491

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Add coefplot and groupedcoefplot for StatisticalModel #491

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9 changes: 8 additions & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,9 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
RecipesPipeline = "01d81517-befc-4cb6-b9ec-a95719d0359c"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
TableOperations = "ab02a1b2-a7df-11e8-156e-fb1833f50b87"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
Widgets = "cc8bc4a8-27d6-5769-a93b-9d913e69aa62"
Expand All @@ -36,16 +38,21 @@ Plots = "1.29"
RecipesBase = "0.6, 1"
RecipesPipeline = "0.2 - 0.6"
Reexport = "0.2, 1"
StatsAPI = "1.2"
StatsBase = "0.32, 0.33, 0.34"
StatsModels = "0.6, 0.7"
TableOperations = "0.2 - 0.3, 1"
Tables = "1"
Widgets = "0.5 - 0.6"
julia = "1.6"

[extras]
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[targets]
test = ["Test", "NaNMath", "StableRNGs"]
test = ["Test", "NaNMath", "StableRNGs", "DataFrames", "CategoricalArrays", "GLM"]
20 changes: 20 additions & 0 deletions README.md
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Expand Up @@ -514,3 +514,23 @@ covellipse([0,2], [2 1; 1 4], n_std=2, aspect_ratio=1, label="cov1")
covellipse!([1,0], [1 -0.5; -0.5 3], showaxes=true, label="cov2")
```
![covariance ellipses](https://user-images.githubusercontent.com/4170948/84170978-f0c2f380-aa82-11ea-95de-ce2fe14e16ec.png)

## Coefficient plot

Coefficients of a `StatsAPI.RegressionModel` can be plotted with their standard deviation to easily visualize effect size.
A collection of models can be plotted together if the response variable is identical.

```julia
using GLM, RobustModels, RDatasets, StatsPlots

iris = dataset("datasets", "iris")
m = lm(@formula(PetalLength ~ SepalLength * Species), iris)
coefplot(m; intercept=true, orientation=:h)
coefplot(m; headers=true)

m2 = quantreg(@formula(PetalLength ~ SepalLength * Species), iris)
groupedcoefplot(m, m2; intercept=true, orientation=:h, label=["lm" "qr"])
groupedcoefplot(m, m2; headers=true, label=["lm" "qr"])
```

![coef plot]()
4 changes: 4 additions & 0 deletions src/StatsPlots.jl
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Expand Up @@ -19,6 +19,9 @@ import Clustering: Hclust, nnodes
using Interpolations
using MultivariateStats: MultivariateStats
using AbstractFFTs: fft, ifft
using StatsAPI
using StatsModels
using StatsModels: hasintercept, TableRegressionModel, kron_insideout, vectorize
import KernelDensity
using NaNMath
@recipe f(k::KernelDensity.UnivariateKDE) = k.x, k.density
Expand Down Expand Up @@ -47,5 +50,6 @@ include("andrews.jl")
include("ordinations.jl")
include("covellipse.jl")
include("errorline.jl")
include("statsmodels.jl")

end # module
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