using StatisticalRethinking
using StatsFuns #logistic() function

d = CSV.read(joinpath(dirname(Base.pathof(StatisticalRethinking)), "..", "data",
    "chimpanzees.csv"), delim=';')
size(d) # Should be 504x8

(504, 8)

pulledleft, condition, prosocleft

@model m10_3(y, x₁, x₂) = begin
    α ~ Normal(0, 10)
    βp ~ Normal(0, 10)
    βpC ~ Normal(0, 10)

    for i ∈ 1:length(y)
        p = logistic(α + (βp + βpC * x₁[i]) * x₂[i])
        y[i] ~ Binomial(1, p)
    end
end

posterior = sample(m10_3(d[:,:pulled_left], d[:,:condition], d[:,:prosoc_left]),
    Turing.NUTS(10000, 1000, 0.95))
describe(posterior)

Empirical Posterior Estimates:
         Mean           SD        Naive SE       MCSE         ESS

α 0.053228176 0.148432403 0.0033190494 0.0072162528 423.091170 βp 0.604297351 0.241527734 0.0054007243 0.0212696753 128.947312 βpC -0.074156932 0.278219321 0.0062211731 0.0279932431 98.779800

#StatisticalRethinking

 Mean StdDev lower 0.89 upper 0.89 n_eff Rhat

a 0.05 0.13 -0.15 0.25 3284 1 bp 0.62 0.22 0.28 0.98 3032 1 bpC -0.11 0.26 -0.53 0.29 3184 1

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