Benchmarks
I have (very much not rigorously) benchmarked Mice.jl using the test dataset [4], and also performed an equivalent benchmark of the R package mice.
System info: Single-threaded execution, Intel® Core™ i7-12700H 2.30GHz CPU, 32GB 4800MHz DDR5 RAM, running Windows 11 version 10.0.22621.
R: version 4.3.1 running mice version 3.16.0. Julia: version 1.9.2 running Mice.jl version 0.1.0.
Imputation (mice)
15 iterations were completed to impute 12 variables (of which 4 binary categorical, 1 other categorical and 7 numeric) using a set of 18 predictors (those 12 variables plus 6 complete variables: 1 binary categorical, 2 other categorical and 3 numeric). In Mice.jl, gcSchedule was set to 0.3.
| Number of imputations | R (mice) (s) | Mice.jl (s) |
|---|---|---|
| 1 | 1.88 | 30.39 |
| 5 | 8.84 | 33.40 |
| 10 | 18.14 | 36.37 |
| 20 | 38.42 | 43.52 |
| 50 | 96.78 | 61.88 |
| 100 | 199.33 | 93.74 |
Why is Mice.jl so slow for small jobs?
Julia is a compiled language. This means that the first time a function is run, it is compiled into machine code, which takes time. Therefore, the first iteration of mice() will be (much) slower in Julia than in R, for example. However, subsequent iterations will be much faster, as all of the required functions are already compiled.
Why is the first iteration so much slower than the rest?
See above.