Profile the likelihood surface of Julia mixed effects models
Source:R/profilelikelihood.R
profilelikelihood.RdProfile the likelihood surface of Julia mixed effects models
Examples
# \donttest{
jlme_setup(restart = TRUE)
#> Starting Julia (v1.10.5) ...
#> Successfully set up Julia connection.
jmod <- jlmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
tidy(jmod)
#> # A tibble: 6 × 7
#> effect group term estimate std.error statistic p.value
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 fixed NA (Intercept) 251. 6.63 37.9 2.02e-314
#> 2 fixed NA Days 10.5 1.50 6.97 3.22e- 12
#> 3 ran_pars Subject sd__(Intercept) 23.8 NA NA NA
#> 4 ran_pars Subject cor__(Intercept).Da… 0.0813 NA NA NA
#> 5 ran_pars Subject sd__Days 5.72 NA NA NA
#> 6 ran_pars Residual sd__Observation 25.6 NA NA NA
prof <- profilelikelihood(jmod)
prof
#> <Julia object of type MixedModelProfile{Float64}>
#> MixedModelProfile -- Table with 11 columns and 176 rows:
#> p ζ β1 β2 σ σ1 σ2 ρ1 ⋯
#> ┌──────────────────────────────────────────────────────────────────────────
#> 1 │ σ -4.365 251.405 10.4673 20.1933 25.5128 5.97319 -0.0159232 ⋯
#> 2 │ σ -3.77902 251.405 10.4673 20.8002 25.3434 5.94786 -0.00711109 ⋯
#> 3 │ σ -3.20526 251.405 10.4673 21.4255 25.1627 5.92133 0.00263598 ⋯
#> 4 │ σ -2.64336 251.405 10.4673 22.0695 24.9702 5.89214 0.0129078 ⋯
#> 5 │ σ -2.09298 251.405 10.4673 22.7328 24.7636 5.86167 0.0241968 ⋯
#> 6 │ σ -1.55378 251.405 10.4673 23.4162 24.5426 5.82871 0.0366992 ⋯
#> 7 │ σ -1.02542 251.405 10.4673 24.12 24.3063 5.79389 0.0501628 ⋯
#> 8 │ σ -0.507597 251.405 10.4673 24.845 24.0525 5.75669 0.0649969 ⋯
#> 9 │ σ 0.0 251.405 10.4673 25.5918 23.7805 5.71683 0.0813321 ⋯
#> 10 │ σ 0.497684 251.405 10.4673 26.3611 23.4885 5.6743 0.0993046 ⋯
#> 11 │ σ 0.985728 251.405 10.4673 27.1534 23.1743 5.62882 0.119204 ⋯
#> 12 │ σ 1.46442 251.405 10.4673 27.9696 22.836 5.5802 0.141262 ⋯
#> 13 │ σ 1.93402 251.405 10.4673 28.8104 22.4727 5.52809 0.165869 ⋯
#> 14 │ σ 2.39481 251.405 10.4673 29.6763 22.0799 5.47226 0.193415 ⋯
#> 15 │ σ 2.84704 251.405 10.4673 30.5684 21.6557 5.41243 0.224387 ⋯
#> 16 │ σ 3.29094 251.405 10.4673 31.4872 21.196 5.34818 0.259442 ⋯
#> 17 │ σ 3.72677 251.405 10.4673 32.4337 20.6956 5.27882 0.299408 ⋯
#> ⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
tidy(prof)
#> # A tibble: 5 × 6
#> effect group term estimate conf.low conf.high
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fixed NA (Intercept) 251. 238. 265.
#> 2 fixed NA Days 10.5 7.36 13.6
#> 3 ran_pars Subject sd__(Intercept) 23.8 14.4 37.7
#> 4 ran_pars Subject sd__Days 5.72 0 8.75
#> 5 ran_pars Residual sd__Observation 25.6 22.9 28.9
stop_julia()
# }