Tables.jl

FlexiChains.Wide(
    chn::Union{<:FlexiChain,<:FlexiSummary};
    split_varnames::Bool=true,
    parameters_only::Bool=true
)

A wrapper struct indicating a 'wide' table format. The exact meaning depends on whether the input is a FlexiChain or a FlexiSummary. A FlexiChain will have each parameter in a separate column; conversely, a FlexiSummary will have each summary statistic in a separate column.

Example (FlexiChain)

Suppose we have a chn::FlexiChain with 2 parameters, x::Float64 and b::Bool, 10 iterations and 2 chains. Then DataFrame(Wide(chn)) will return a DataFrame that looks like the following. Each parameter is a different column, and the iter and chain dimensions are represented as separate columns as well; iter varies faster than chain.

20×4 DataFrame Row │ iter   chain  x          b     │ Int64  Int64  Float64    Bool─────┼────────────────────────────────   1 │     1      1  -1.38809   false   2 │     2      1  -0.511805   true   3 │     3      1  -1.37277   false  ⋮  │   ⋮      ⋮        ⋮        ⋮  18 │     8      2   2.31312   false  19 │     9      2   1.58254    true  20 │    10      2  -1.14516   false

Keyword arguments

• split_varnames: whether to split array-valued parameters into scalar leaves. If true (the default), then array-valued parameters are split into scalar leaves, e.g. a vector-valued parameter x would be split into x[1], x[2], etc.

• parameters_only: whether to include only parameters (and skip extras) in the resulting table. Defaults to true.

Example (FlexiSummary)

julia> df = DataFrame(Wide(summarystats(chn)))2×10 DataFrame Row │ param     mean       std       mcse      ess_bulk  ess_tail  rh ⋯     │ VarName…  Float64    Float64   Float64   Float64   Float64   Fl ⋯─────┼──────────────────────────────────────────────────────────────────   1 │ x         -0.253812  0.987327  0.193554   26.0206    25.641     ⋯   2 │ b          0.5       0.512989  0.113426   20.4545   NaN      Na                                                       4 columns omittedjulia> df = DataFrame(Wide(mean(chn)))2×2 DataFrame Row │ param     mean     │ VarName…  Float64─────┼─────────────────────   1 │ x         -0.253812   2 │ b          0.5

FlexiChains.Long(
    chn::FlexiChain;
    split_varnames::Bool=true,
    parameters_only::Bool=true
)

A wrapper struct indicating that a FlexiChain should be converted to a 'long' table format, where all values are stacked into a single column, and there is an additional column indicating the parameter name.

Example

Suppose we have a chn::FlexiChain with 2 parameters, x::Float64 and b::Bool, 10 iterations and 2 chains. Then DataFrame(Long(chn)) will return a DataFrame that looks like the following. The iter and chain dimensions are represented as separate columns as before, but now the parameter names are stacked into a single param column, and the values are stacked into a single value column.

The iter column varies faster than the chain column, which in turn varies faster than the param column.

40×4 DataFrame Row │ iter   chain  param     value     │ Int64  Int64  VarName…  Float64─────┼─────────────────────────────────   1 │     1      1  x         -1.38809   2 │     2      1  x         -0.511805   3 │     3      1  x         -1.37277  ⋮  │   ⋮      ⋮      ⋮         ⋮  38 │     8      2  b          0.0  39 │     9      2  b          1.0  40 │    10      2  b          0.0

Keyword arguments

• split_varnames: whether to split array-valued parameters into scalar leaves. If true (the default), then array-valued parameters are split into scalar leaves, e.g. a vector-valued parameter x would be split into x[1], x[2], etc.

• parameters_only: whether to include only parameters (and skip extras) in the resulting table. Defaults to true.

Additionally, when parameters_only=true (the default), the Parameter wrapper is stripped from keys. Otherwise, the Parameter/Extra wrappers are retained. If you want to unwrap them, you can use FlexiChains.get_name on the param column of the resulting table.