Last updated: 2019-09-28

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Knit directory: 2019-feature-selection/

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    Modified:   .Rprofile
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html 0b18307 pat-s 2019-09-21 Build site.
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Rmd 518d0cb pat-s 2019-09-01 style files using tidyverse style

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-16

Exploring model residuals

Visualizing model residuals of LASSO and RF to see how they differ. The LASSO “predicted vs. fitted” plot shows limited model power.

Lasso model with no transformation of the response variable

Warning: namespace 'glmnet' is not available and has been replaced
by .GlobalEnv when processing object ''

Version Author Date
0b18307 pat-s 2019-09-21
aa0486b pat-s 2019-09-15

RF model with no transformation of the response variable

Version Author Date
0b18307 pat-s 2019-09-21
aa0486b pat-s 2019-09-15

Variable Transformations

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.

Power transformation

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: /opt/R/3.5.2/lib64/R/lib/libRlapack.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):
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[10] smoof_1.5.1        plyr_1.8.4         backports_1.1.3   
[13] evaluate_0.13      httr_1.4.0         pillar_1.3.1      
[16] rlang_0.3.4        lazyeval_0.2.1     misc3d_0.8-4      
[19] data.table_1.12.0  whisker_0.3-2      Matrix_1.2-15     
[22] checkmate_1.9.1    rmarkdown_1.13     labeling_0.3      
[25] mco_1.0-15.1       splines_3.5.2      stringr_1.4.0     
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[73] plotly_4.8.0       knitr_1.23