# m5.5s.jl

using Pkg, DrWatson

using MonteCarloMeasurements
using StanSample
using StatisticalRethinking

# ### snippet 5.29

println()
df = CSV.read(sr_datadir("milk.csv"), DataFrame; delim=';');
df = filter(row -> !(row[:neocortex_perc] == "NA"), df);
df[!, :neocortex_perc] = parse.(Float64, df[:, :neocortex_perc])
df[!, :lmass] = log.(df[:, :mass])
scale!(df, [:kcal_per_g, :neocortex_perc, :lmass])

stan5_5 = "
data {
    int < lower = 1 > N; // Sample size
    vector[N] K; // Outcome
    vector[N] NC; // Predictor
}

parameters {
    real a; // Intercept
    real bN; // Slope (regression coefficients)
    real < lower = 0 > sigma;    // Error SD
}

model {
    vector[N] mu;               // mu is a vector
    a ~ normal(0, 0.2);           //Priors
    bN ~ normal(0, 0.5);
    sigma ~ exponential(1);
    mu = a + bN * NC;
    K ~ normal(mu , sigma);     // Likelihood
}
";

# Define the SampleModel and set the output format to :mcmcchains.

m5_5s = SampleModel("m5.5", stan5_5);

# Input data for cmdstan

m5_5_data = Dict("N" => size(df, 1), "NC" => df[!, :neocortex_perc_s],
    "K" => df[!, :kcal_per_g_s]);

# Sample using StanSample

rc5_5s = stan_sample(m5_5s, data=m5_5_data);

if success(rc5_5s)

    # Describe the draws

    part5_5s = read_samples(m5_5s, :particles)
    part5_5s |> display

    retinking = "
            mean   sd  5.5% 94.5%
    a     0.04 0.15 -0.21  0.29
    bN    0.13 0.22 -0.22  0.49
    sigma 1.00 0.16  0.74  1.26
    "

end
