V4_Bioc2mlr_on_steroids_resample.RmdThis vignette will demonstrate how to leverage the automate built-in tools in mlr, with the converted S4 assay container classes. Specifically, it will cover benchmarking, tuning parameters, and resampling (cross-validation, bootstraping)
sessionInfo()
#> R version 3.5.0 (2018-04-23)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 14393)
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#> Matrix products: default
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#> locale:
#> [1] LC_COLLATE=English_United States.1252
#> [2] LC_CTYPE=English_United States.1252
#> [3] LC_MONETARY=English_United States.1252
#> [4] LC_NUMERIC=C
#> [5] LC_TIME=English_United States.1252
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#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
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#> other attached packages:
#> [1] tibble_2.0.1 mlr_2.13.9000 ParamHelpers_1.12 Bioc2mlr_0.1.0
#> [5] purrr_0.2.5 magrittr_1.5 dplyr_0.7.8
#>
#> loaded via a namespace (and not attached):
#> [1] parallelMap_1.4 Rcpp_1.0.0 plyr_1.8.4
#> [4] compiler_3.5.0 pillar_1.3.1 bindr_0.1.1
#> [7] tools_3.5.0 digest_0.6.18 lattice_0.20-35
#> [10] gtable_0.2.0 evaluate_0.12 memoise_1.1.0
#> [13] checkmate_1.9.1 pkgconfig_2.0.2 rlang_0.3.1
#> [16] Matrix_1.2-14 fastmatch_1.1-0 rstudioapi_0.7
#> [19] commonmark_1.5 parallel_3.5.0 yaml_2.2.0
#> [22] pkgdown_1.3.0 xfun_0.4 bindrcpp_0.2.2
#> [25] stringr_1.3.1 roxygen2_6.1.1 xml2_1.2.0
#> [28] knitr_1.21 desc_1.2.0 fs_1.2.3
#> [31] grid_3.5.0 rprojroot_1.3-2 tidyselect_0.2.5
#> [34] glue_1.3.0 data.table_1.12.0 R6_2.3.0
#> [37] survival_2.41-3 rmarkdown_1.11 ggplot2_3.1.0
#> [40] splines_3.5.0 scales_1.0.0 backports_1.1.3
#> [43] BBmisc_1.11 htmltools_0.3.6 MASS_7.3-49
#> [46] assertthat_0.2.0 colorspace_1.4-0 stringi_1.2.4
#> [49] lazyeval_0.2.1 munsell_0.5.0 crayon_1.3.4