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* Add compat entry for StatsFuns * Let height correspond to Edition 2 * Fix ignoring-gender-admit * Fix Africa
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,89 +1,4 @@ | ||
| +++ | ||
| title = "Africa" | ||
| +++ | ||
| @def title = "Africa" | ||
| @def showall = true | ||
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| This is the first Stan model in Statistical Rethinking Edition 1 (page 249). | ||
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| \toc | ||
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| ## Data | ||
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| ```julia:data | ||
| using DataFrames | ||
| using Distributions | ||
| using Turing | ||
| using TuringModels # hide | ||
| import CSV | ||
| data_path = joinpath(TuringModels.project_root, "data", "rugged.csv") | ||
| df = CSV.read(data_path, DataFrame) | ||
| df.log_gdp = [log(x) for x in df.rgdppc_2000] | ||
| TODO = "Replace by dropmissing or something similar." | ||
| notisnan(e) = !ismissing(e) | ||
| df = df[map(notisnan, df[:, :rgdppc_2000]), :]; | ||
| df = select(df, :log_gdp, :rugged, :cont_africa) | ||
| write_csv(name, data) = CSV.write(joinpath(@OUTPUT, "$name.csv"), data) # hide | ||
| write_csv("data", df) # hide | ||
| ``` | ||
| \output{data} | ||
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| DataFrame `df` is shown in [Appendix I](#appendix_i). | ||
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| ## Model | ||
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| ```julia:model | ||
| @model function model_fn(y, x₁, x₂) | ||
| σ ~ truncated(Cauchy(0, 2), 0, Inf) | ||
| βAR ~ Normal(0, 10) | ||
| βR ~ Normal(0, 10) | ||
| βA ~ Normal(0, 10) | ||
| α ~ Normal(0, 100) | ||
| μ = α .+ βR * x₁ .+ βA * x₂ .+ βAR * x₁ .* x₂ | ||
| y .~ Normal.(μ, σ) | ||
| end | ||
| model = model_fn(df.log_gdp, df.rugged, df.cont_africa) | ||
| chains = sample(model, NUTS(0.65), 1000) | ||
| ``` | ||
| \output{model} | ||
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| ## Output | ||
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| \defaultoutput{} | ||
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| ## Original output | ||
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| ``` | ||
| Iterations = 1:1000 | ||
| Thinning interval = 1 | ||
| Chains = 1,2,3,4 | ||
| Samples per chain = 1000 | ||
| Empirical Posterior Estimates: | ||
| Mean SD Naive SE MCSE ESS | ||
| a 9.22360053 0.139119116 0.0021996664 0.0034632816 1000 | ||
| bR -0.20196346 0.076106388 0.0012033477 0.0018370185 1000 | ||
| bA -1.94430980 0.227080488 0.0035904578 0.0057840746 1000 | ||
| bAR 0.39071684 0.131889143 0.0020853505 0.0032749642 1000 | ||
| sigma 0.95036370 0.052161768 0.0008247500 0.0009204073 1000 | ||
| Quantiles: | ||
| 2.5% 25.0% 50.0% 75.0% 97.5% | ||
| a 8.95307475 9.12719750 9.2237750 9.31974000 9.490234250 | ||
| bR -0.35217930 -0.25334425 -0.2012855 -0.15124725 -0.054216855 | ||
| bA -2.39010825 -2.09894500 -1.9432550 -1.78643000 -1.513974250 | ||
| bAR 0.13496995 0.30095575 0.3916590 0.47887625 0.650244475 | ||
| sigma 0.85376115 0.91363250 0.9484920 0.98405750 1.058573750 | ||
| ``` | ||
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| ## Appendix | ||
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| ### Appendix I | ||
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| \tableinput{}{./code/output/data.csv} | ||
| \literate{/scripts/africa.jl} |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,34 +1,6 @@ | ||
| +++ | ||
| title = "Basic Example" | ||
| showall = true | ||
| +++ | ||
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| \toc | ||
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| ## Model definition | ||
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| We define a simple Normal model with unknown mean and variance. | ||
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| ```julia:model | ||
| import CSV | ||
| using DataFrames | ||
| using StatsPlots | ||
| using Turing | ||
| @model gdemo(x, y) = begin | ||
| s ~ InverseGamma(2, 3) | ||
| m ~ Normal(0, sqrt(s)) | ||
| x ~ Normal(m, sqrt(s)) | ||
| y ~ Normal(m, sqrt(s)) | ||
| end | ||
| ``` | ||
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| and run the sampler: | ||
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| ```julia:sampler | ||
| chains = sample(gdemo(1.5, 2), NUTS(0.65), 1000) | ||
| ``` | ||
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| ## Output | ||
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| \defaultoutput{} | ||
| \literate{/scripts/basic-example.jl} |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,7 +1,6 @@ | ||
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| title = "Globe tossing" | ||
| showall = true | ||
| reeval = true | ||
| +++ | ||
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| \literate{/scripts/globe-tossing.jl} |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,103 +1,29 @@ | ||
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| title = "Gaussian model of height" | ||
| showall = true | ||
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| This model is based on data of the !Kung San people \citep{pHowell2010}. | ||
| The prior assumes that the height of a man is 178 cm because McElreath has that lenght, so it is a reasonable prior for the height. | ||
| The prior assumes that the height of a man is most likely to be 178 cm because McElreath has that lenght. | ||
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| McElreath defines the model as | ||
| In Edition 2, McElreath defines the model as | ||
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| $$ | ||
| \begin{aligned} | ||
| h_i &\sim \text{Normal}(\mu, \sigma) \\ | ||
| \mu &\sim \text{Normal}(178, 20) \\ | ||
| \sigma &\sim \text{Uniform}(0, 50) | ||
| \end{aligned} | ||
| \begin{aligned} | ||
| h_i &\sim \text{Normal}(\mu, \sigma) \\ | ||
| \mu &\sim \text{Normal}(178, 0.1) \\ | ||
| \sigma &\sim \text{Uniform}(0, 50) | ||
| \end{aligned} | ||
| $$ | ||
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| \toc | ||
| \toc | ||
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| ## Model definition | ||
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| ```julia:height | ||
| import CSV | ||
| using DataFrames | ||
| using StatsPlots | ||
| using Turing | ||
| using TuringModels | ||
| data_path = joinpath(TuringModels.project_root, "data", "Howell1.csv") | ||
| df = CSV.read(data_path, DataFrame; delim=';') | ||
| # Use only adults and center the weight observations | ||
| df2 = filter(row -> row.age >= 18, df) | ||
| mean_weight = mean(df2.weight) | ||
| df2.weight_c = df2.weight .- mean_weight | ||
| @model function line(x, y) | ||
| alpha ~ Normal(178.0, 100.0) | ||
| beta ~ Normal(0.0, 10.0) | ||
| sigma ~ Uniform(0, 50) | ||
| mu = alpha .+ beta*x | ||
| y .~ Normal.(mu, sigma) | ||
| end | ||
| x = df2.weight_c | ||
| y = df2.height | ||
| chains = sample(line(x, y), NUTS(0.65), 1000) | ||
| ``` | ||
| \output{height} | ||
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| ```julia:write_helper | ||
| # hideall | ||
| output_dir = @OUTPUT | ||
| function write_svg(name, p) | ||
| fig_path = joinpath(output_dir, "$name.svg") | ||
| StatsPlots.savefig(fig_path) | ||
| end | ||
| ``` | ||
| \output{write_helper} | ||
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| ## Output | ||
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| \defaultoutput{} | ||
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| ## Original output | ||
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| ``` | ||
| Iterations = 1:1000 | ||
| Thinning interval = 1 | ||
| Chains = 1,2,3,4 | ||
| Samples per chain = 1000 | ||
| Empirical Posterior Estimates: | ||
| Mean SD Naive SE MCSE ESS | ||
| alpha 154.597086 0.27326431 0.0043206882 0.0036304132 1000 | ||
| beta 0.906380 0.04143488 0.0006551430 0.0006994720 1000 | ||
| sigma 5.106643 0.19345409 0.0030587777 0.0032035103 1000 | ||
| Quantiles: | ||
| 2.5% 25.0% 50.0% 75.0% 97.5% | ||
| alpha 154.0610000 154.4150000 154.5980000 154.7812500 155.1260000 | ||
| beta 0.8255494 0.8790695 0.9057435 0.9336445 0.9882981 | ||
| sigma 4.7524368 4.9683400 5.0994450 5.2353100 5.5090128 | ||
| ``` | ||
| \literate{/scripts/height.jl} | ||
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| ## References | ||
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| \biblabel{pHowell2010}{Howell, 2010} | ||
| Howell, N. (2010). | ||
| Life Histories of the Dobe !Kung: Food, Fatness, and Well-being over the Life-span. | ||
| Origins of Human Behavior and Culture. | ||
| University of California Press | ||
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| ## Appendix | ||
| ### df2 | ||
| ```julia:data | ||
| write_csv(name, data) = CSV.write(joinpath(@OUTPUT, "$name.csv"), data) # hide | ||
| write_csv("data", # hide | ||
| df2 | ||
| ) # hide | ||
| ``` | ||
| \output{data} | ||
| \tableinput{}{./code/output/data.csv} |
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