This page shows an example of plotting MCMCChains.jl with Makie.jl. The example is meant to provide an useful basis to build upon. Let's define some random chain and load the required packages:
using MCMCChains
chns = Chains(randn(300, 5, 3), [:A, :B, :C, :D, :E])
A basic way to visualize the chains is to show the drawn samples at each iteration. Colors depict different chains.
using CairoMakie
CairoMakie.activate!(; type="svg")
params = names(chns, :parameters)
n_chains = length(chains(chns))
n_samples = length(chns)
fig = Figure(; resolution=(1_000, 800))
for (i, param) in enumerate(params)
ax = Axis(fig[i, 1]; ylabel=string(param))
for chain in 1:n_chains
values = chns[:, param, chain]
lines!(ax, 1:n_samples, values; label=string(chain))
end
hideydecorations!(ax; label=false)
if i < length(params)
hidexdecorations!(ax; grid=false)
else
ax.xlabel = "Iteration"
end
end
fig
Next, we can add a second row of plots next to it which show the density estimate for these samples:
for (i, param) in enumerate(params)
ax = Axis(fig[i, 2]; ylabel=string(param))
for chain in 1:n_chains
values = chns[:, param, chain]
density!(ax, values; label=string(chain))
end
hideydecorations!(ax)
if i < length(params)
hidexdecorations!(ax; grid=false)
else
ax.xlabel = "Parameter estimate"
end
end
axes = [only(contents(fig[i, 2])) for i in 1:length(params)]
linkxaxes!(axes...)
fig
Finally, let's add a simple legend.
Thanks to setting label
above, this legend will have the right labels:
axislegend(first(axes))
fig