Last updated: 2019-09-21
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Knit directory: 2019-feature-selection/
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Modified: R/06-mlr-paper.R
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This document originated from the fear of having a response variable which is not normally distributed “enough”.
The response variable looks as follows:
| Version | Author | Date |
|---|---|---|
| aa0486b | pat-s | 2019-09-15 |
When applying the Shapiro-Wilk test we get
Shapiro-Wilk normality test
data: vi_data$defoliation
W = 0.86183, p-value < 2.2e-16Visualizing model residuals of LASSO and RF to see how they differ. The LASSO “predicted vs. fitted” plot shows limited model power.
| Version | Author | Date |
|---|---|---|
| aa0486b | pat-s | 2019-09-15 |
| Version | Author | Date |
|---|---|---|
| aa0486b | pat-s | 2019-09-15 |
The following transformations of the response variable were done to check if it they have an effect on the “residuals vs. fitted” and “QQ-Plot” shown above.
One option to enforce more normality of a variable is by applying a power transformation. The Box-Cox power transformation estimates a lambda value from the variable. Next, the transformation can be applied via
\[(y^lambda - 1) / lambda\]
There is a Stackoverflow question that shows how to do this.
R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /opt/R/3.5.2/lib64/R/lib/libRblas.so
LAPACK: /usr/lib64/libopenblaso-r0.3.3.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.1.0 tidyselect_0.2.5 drake_7.5.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.2 txtq_0.1.4 lattice_0.20-38
[4] tidyr_1.0.0 foreach_1.4.4 glmnet_2.0-16
[7] assertthat_0.2.0 zeallot_0.1.0 rprojroot_1.3-2
[10] digest_0.6.18 R6_2.4.0 smoof_1.5.1
[13] plyr_1.8.4 backports_1.1.3 evaluate_0.13
[16] httr_1.4.0 pillar_1.3.1 rlang_0.4.0
[19] lazyeval_0.2.1 misc3d_0.8-4 data.table_1.12.0
[22] whisker_0.3-2 Matrix_1.2-15 checkmate_1.9.1
[25] rmarkdown_1.13 labeling_0.3 mco_1.0-15.1
[28] splines_3.5.2 stringr_1.4.0 htmlwidgets_1.3
[31] igraph_1.2.4 munsell_0.5.0 compiler_3.5.2
[34] xfun_0.5 DiceKriging_1.5.6 ParamHelpers_1.12
[37] pkgconfig_2.0.2 mlr_2.15.0.9000 BBmisc_1.11
[40] htmltools_0.3.6 tibble_2.1.3 workflowr_1.4.0
[43] codetools_0.2-16 viridisLite_0.3.0 crayon_1.3.4
[46] dplyr_0.8.3 withr_2.1.2 grid_3.5.2
[49] jsonlite_1.6 gtable_0.2.0 lifecycle_0.1.0
[52] git2r_0.24.0 magrittr_1.5 storr_1.2.1
[55] mlrMBO_1.1.2 scales_1.0.0 cli_1.1.0
[58] stringi_1.3.1 fs_1.2.6 parallelMap_1.4
[61] lhs_1.0.1 filelock_1.0.2 vctrs_0.2.0
[64] fastmatch_1.1-0 plot3D_1.1.1 RColorBrewer_1.1-2
[67] iterators_1.0.10 tools_3.5.2 RJSONIO_1.3-1.1
[70] glue_1.3.0 purrr_0.3.0 parallel_3.5.2
[73] survival_2.43-3 yaml_2.2.0 colorspace_1.4-0
[76] base64url_1.4 plotly_4.8.0 knitr_1.23