Helpers for converting model specifications in R to Julia equivalents

Usage

jl_formula(formula)

jl_contrasts(df, cols = NULL, show_code = FALSE)

jl_data(df)

jl_family(family = c("gaussian", "binomial", "poisson"))

Arguments

formula: A string or formula object
df: A data frame
cols: A subset of columns to make contrast specifiations for
show_code: Whether to print corresponding Julia code as a side-effect
family: The distributional family as string or <family> object

Value

A Julia object of type <JuliaProxy>

Examples

# \donttest{
jlme_setup(restart = TRUE)
#> Starting Julia (v1.10.5) ...
#> Successfully set up Julia connection. (30s)

# Set up model data in R
x <- mtcars
x$cyl_helm <- factor(x$cyl)
contrasts(x$cyl_helm) <- contr.helmert(3)
colnames(contrasts(x$cyl_helm)) <- c("4vs6", "4&6vs8")

# Formula conversion with
julia_formula <- jl_formula(mpg ~ am * cyl_helm)
julia_formula
#> <Julia object of type FormulaTerm{Term, Tuple{Term, Term, InteractionTerm{Tuple{Term, Term}}}}>
#> FormulaTerm
#> Response:
#>   mpg(unknown)
#> Predictors:
#>   am(unknown)
#>   cyl_helm(unknown)
#>   am(unknown) & cyl_helm(unknown)

# Data frame conversion
julia_data <- jl_data(x)
julia_data
#> <Julia object of type Table{@NamedTuple{mpg::Float64, cyl::Float64, disp::Float64, hp::Float64, drat::Float64, wt::Float64, qsec::Float64, vs::Float64, am::Float64, gear::Float64, carb::Float64, cyl_helm::String}, 1, @NamedTuple{mpg::Vector{Float64}, cyl::Vector{Float64}, disp::Vector{Float64}, hp::Vector{Float64}, drat::Vector{Float64}, wt::Vector{Float64}, qsec::Vector{Float64}, vs::Vector{Float64}, am::Vector{Float64}, gear::Vector{Float64}, carb::Vector{Float64}, cyl_helm::Vector{String}}}>
#> Table with 12 columns and 32 rows:
#>       mpg   cyl  disp   hp     drat  wt     qsec   vs   am   gear  carb  ⋯
#>     ┌─────────────────────────────────────────────────────────────────────
#>  1  │ 21.0  6.0  160.0  110.0  3.9   2.62   16.46  0.0  1.0  4.0   4.0   ⋯
#>  2  │ 21.0  6.0  160.0  110.0  3.9   2.875  17.02  0.0  1.0  4.0   4.0   ⋯
#>  3  │ 22.8  4.0  108.0  93.0   3.85  2.32   18.61  1.0  1.0  4.0   1.0   ⋯
#>  4  │ 21.4  6.0  258.0  110.0  3.08  3.215  19.44  1.0  0.0  3.0   1.0   ⋯
#>  5  │ 18.7  8.0  360.0  175.0  3.15  3.44   17.02  0.0  0.0  3.0   2.0   ⋯
#>  6  │ 18.1  6.0  225.0  105.0  2.76  3.46   20.22  1.0  0.0  3.0   1.0   ⋯
#>  7  │ 14.3  8.0  360.0  245.0  3.21  3.57   15.84  0.0  0.0  3.0   4.0   ⋯
#>  8  │ 24.4  4.0  146.7  62.0   3.69  3.19   20.0   1.0  0.0  4.0   2.0   ⋯
#>  9  │ 22.8  4.0  140.8  95.0   3.92  3.15   22.9   1.0  0.0  4.0   2.0   ⋯
#>  10 │ 19.2  6.0  167.6  123.0  3.92  3.44   18.3   1.0  0.0  4.0   4.0   ⋯
#>  11 │ 17.8  6.0  167.6  123.0  3.92  3.44   18.9   1.0  0.0  4.0   4.0   ⋯
#>  12 │ 16.4  8.0  275.8  180.0  3.07  4.07   17.4   0.0  0.0  3.0   3.0   ⋯
#>  13 │ 17.3  8.0  275.8  180.0  3.07  3.73   17.6   0.0  0.0  3.0   3.0   ⋯
#>  14 │ 15.2  8.0  275.8  180.0  3.07  3.78   18.0   0.0  0.0  3.0   3.0   ⋯
#>  15 │ 10.4  8.0  472.0  205.0  2.93  5.25   17.98  0.0  0.0  3.0   4.0   ⋯
#>  16 │ 10.4  8.0  460.0  215.0  3.0   5.424  17.82  0.0  0.0  3.0   4.0   ⋯
#>  17 │ 14.7  8.0  440.0  230.0  3.23  5.345  17.42  0.0  0.0  3.0   4.0   ⋯
#>  ⋮  │  ⋮     ⋮     ⋮      ⋮     ⋮      ⋮      ⋮     ⋮    ⋮    ⋮     ⋮    ⋱

# Contrasts construction (`show_code = TRUE` pretty prints the Julia code)
julia_contrasts <- jl_contrasts(x, show_code = TRUE)
#> Dict(
#>     :cyl_helm => HypothesisCoding(
#>         [
#>             -1/2  1/2   0
#>             -1/6 -1/6 1/3
#>         ];
#>         levels = ["4", "6", "8"],
#>         labels = ["4vs6", "4&6vs8"],
#>     ),
#> )
julia_contrasts
#> <Julia object of type Dict{Symbol, HypothesisCoding{Matrix{Float64}, Matrix{Float64}}}>
#> Dict{Symbol, HypothesisCoding{Matrix{Float64}, Matrix{Float64}}} with 1 entry:
#>   :cyl_helm => HypothesisCoding{Matrix{Float64}, Matrix{Float64}}([-0.5 0.5 0.0…

# Family conversion
julia_family <- jl_family("binomial")
julia_family
#> <Julia object of type Bernoulli{Float64}>
#> Bernoulli{Float64}(p=0.5)

stop_julia()
# }