This vignette showcases ways of interacting with pointers to jlmerclusterperm Julia objects from R using JuliaConnectoR.
See more tutorials and vignettes on the Articles page.
Setup
library(jlmerclusterperm)
jlmerclusterperm_setup(verbose = FALSE)
julia_progress(show = FALSE)Interacting with Julia objects
All jlmerclusterperm functions collect Julia objects as
R objects, except jlmer() and to_jlmer() which
return GLM.jl or MixedModels.jl fitted model objects.
jmod <- to_jlmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
jmod
#> <Julia object of type LinearMixedModel{Float64}>
#> Variance components:
#> Column Variance Std.Dev. Corr.
#> Subject (Intercept) 565.51067 23.78047
#> Days 32.68212 5.71683 +0.08
#> Residual 654.94145 25.59182
#> ──────────────────────────────────────────────────
#> Coef. Std. Error z Pr(>|z|)
#> ──────────────────────────────────────────────────
#> (Intercept) 251.405 6.63226 37.91 <1e-99
#> Days 10.4673 1.50224 6.97 <1e-11
#> ──────────────────────────────────────────────────You can use functions from JuliaConnectoR
to interact with these (pointers to) Julia objects:
library(JuliaConnectoR)
juliaLet("x.rePCA", x = jmod)
#> <Julia object of type NamedTuple{(:Subject,), Tuple{Vector{Float64}}}>
#> (Subject = [0.5406660682947617, 1.0],)
juliaCall("issingular", jmod)
#> [1] FALSEYou can also call functions from other Julia packages that you
already have installed. For example, Effects.jl
for its marginaleffects/emmeans-like
features:
# juliaEval('using Pkg; Pkg.activate(); Pkg.add("Effects")')
juliaEval("using Effects")
juliaLet("effects(x.namedelements, y)", x = list(Days = 2:5), y = jmod)
#> <Julia object of type DataFrame>
#> 4×5 DataFrame
#> Row │ Days Reaction err lower upper
#> │ Int64 Float64 Float64 Float64 Float64
#> ─────┼────────────────────────────────────────────
#> 1 │ 2 272.34 7.09427 265.245 279.434
#> 2 │ 3 282.807 7.70544 275.102 290.512
#> 3 │ 4 293.274 8.55557 284.719 301.83
#> 4 │ 5 303.742 9.58128 294.16 313.323Note that jlmer() and to_lmer() are just
conveniences for sanity checking the steps of a cluster-based
permutation test, and thus should not be used as a general interface to
fitting regression models in Julia for more serious analyses.
Julia global options
All functions involved in the cluster-based permutation analysis
print progress bars that update in real time in the console. The
function julia_progress() controls whether to
show the progress bar and the width of the
printed progress bar.
# Set Julia progress options and save old state
old_progress_opts <- julia_progress(show = FALSE, width = 30)
old_progress_opts
#> $show
#> [1] TRUE
#>
#> $width
#> [1] 50
julia_progress()
#> $show
#> [1] FALSE
#>
#> $width
#> [1] 30
# Restored old state
julia_progress(old_progress_opts)
identical(old_progress_opts, julia_progress())
#> [1] TRUE
# The syntax to reset progress options
julia_progress(show = TRUE, width = "auto")
julia_progress()The Julia environment
The following packages are loaded into the Julia environment via
jlmerclusterperm_setup():
cat(readLines(system.file("julia/Project.toml", package = "jlmerclusterperm")), sep = "\n")
#> [deps]
#> DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
#> Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
#> GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
#> JlmerClusterPerm = "2a59065a-9564-4903-82dd-0e42ce19d0e1"
#> MixedModels = "ff71e718-51f3-5ec2-a782-8ffcbfa3c316"
#> ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
#> Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
#> Random123 = "74087812-796a-5b5d-8853-05524746bad3"
#> StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
#> StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
#> Suppressor = "fd094767-a336-5f1f-9728-57cf17d0bbfb"
#>
#> # add DataFrames@1.5, GLM@1.8, MixedModels@4, ProgressMeter@1.7,
#> # Random123@1.6, StatsBase@0.33, StatsModels@0.7, Suppressor@0.2
#> # add Distributions, Random
#> # dev JlmerClusterPerm
#>
#> [compat]
#> DataFrames = "~1.3.0"
#> GLM = "~1.8"
#> JlmerClusterPerm = "=0.1.1"
#> MixedModels = "4"
#> ProgressMeter = "~1.7"
#> Random123 = "~1.6"
#> StatsBase = "~0.33"
#> StatsModels = "~0.7"
#> Suppressor = "~0.2"
#> julia = "1.8"Optional configurations
For speed-ups in model fitting, install MKL.jl (Intel
core) or AppleAccelerate.jl
(Mac) and load it on start by modifying the startup.jl
file.
For example, on my Windows machine the startup file is located at:
JuliaConnectoR::juliaCall("Base._local_julia_startup_file")
#> "C:/Users/jchoe/.julia/config/startup.jl"And there I load MKL.jl:
if contains(first(Sys.cpu_info()).model, "Intel") using MKL end