Fit a Julia regression model using jlmer specifications
Usage
jlmer(jlmer_spec, family = c("gaussian", "binomial"), ..., progress = FALSE)Arguments
- jlmer_spec
Data prepped for jlmer from
make_jlmer_spec()- family
A GLM family. Currently supports "gaussian" and "binomial".
- ...
Optional arguments passed to Julia for model fitting.
- progress
If
TRUE, prints the timing of iterations.
Examples
# \donttest{
# \dontshow{
options("jlmerclusterperm.nthreads" = 2)
jlmerclusterperm_setup(cache_dir = tempdir(), verbose = FALSE)
julia_progress(show = FALSE)
# }
# Fitting a regression model with a specification object
spec <- make_jlmer_spec(weight ~ 1 + Diet, ChickWeight)
jlmer(spec)
#> <Julia object of type StatsModels.TableRegressionModel>
#> ────────────────────────────────────────────────────────────────────────
#> Coef. Std. Error z Pr(>|z|) Lower 95% Upper 95%
#> ────────────────────────────────────────────────────────────────────────
#> (Intercept) 102.645 4.67395 21.96 <1e-99 93.4847 111.806
#> Diet2 19.9712 7.86744 2.54 0.0111 4.55132 35.3911
#> Diet3 40.3045 7.86744 5.12 <1e-06 24.8847 55.7244
#> Diet4 32.6173 7.91046 4.12 <1e-04 17.113 48.1215
#> ────────────────────────────────────────────────────────────────────────
# `lm()` equivalent
summary(lm(weight ~ 1 + Diet, ChickWeight))$coef
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 102.64545 4.673954 21.961161 4.712762e-78
#> Diet2 19.97121 7.867437 2.538465 1.139708e-02
#> Diet3 40.30455 7.867437 5.122958 4.113938e-07
#> Diet4 32.61726 7.910461 4.123307 4.286352e-05
# \dontshow{
JuliaConnectoR::stopJulia()
# }
# }