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Rmd 53a6b62 pat-s 2020-02-28 save feature-importance state
Rmd 518d0cb pat-s 2019-09-01 style files using tidyverse style
Rmd d7df860 pat-s 2019-07-10 add feat imp plan
Rmd 3999cd9 pat-s 2019-06-30 add feature importance report

Preview the ordered feature importance results for datasets “HR” and “VI”.

# A tibble: 122 x 5
   feature importance wavelength numeric_id  rank
   <chr>        <dbl>      <dbl>      <dbl> <int>
 1 B69          1.57        724          69     1
 2 B70          1.28        729.         70     2
 3 B126         1.26        995.        126     3
 4 B115         1.11        942.        115     4
 5 B68          1.01        719.         68     5
 6 B7           0.953       430.          7     6
 7 B71          0.951       734.         71     7
 8 B67          0.874       714.         67     8
 9 B113         0.837       933         113     9
10 B124         0.773       985.        124    10
# … with 112 more rows
# A tibble: 89 x 3
   feature    importance  rank
   <chr>           <dbl> <int>
 1 Vogelmann2      1.79      1
 2 Vogelmann4      1.47      2
 3 Vogelmann       0.914     3
 4 NPCI            0.717     4
 5 Vogelmann3      0.707     5
 6 D2              0.657     6
 7 Datt3           0.637     7
 8 PWI             0.545     8
 9 SR7             0.526     9
10 SRPI            0.509    10
# … with 79 more rows

Create a virtual Spectral Signature (mean) of vegetation using PROSAIL.

PROSAIL is a algorithm simulating Spectral Signature (mean)s of vegetation, see ?hsdar::PROSAIL. Reflectance is scaled to 0-10 to be able to plot it in the same plot as the feature importance rankings -> the axis limits for the y and z axis needs to match.

PROSAIL returns a Spectral Signature (mean) from 400 nm to 2500 nm -> we take the values only and subset to 400 nm - 1000 nm. Because we order from 1 - 10 with 1 being the best rank, we have to reverse the scaling of the reflectance values.

Next we bind the simulated data with the feature importance rankings. To join both data.frames we need to round the reflectance centers of the bands to integers to match with the reflectance values created by PROSAIL.

To label only a subset of the data, a custom data.frame is created.

P1 Main plot

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Plots by dataset

HR

P2 Absolute permutation based Var Imp

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VI

P3 Absolute permutation based Var Imp

Version Author Date
ac809cf pat-s 2020-03-03

Vogelmann2 \((R_{734}-R_{747})/(R_{715}+R_{726})\) Vogelmann et al. (1993)

Vogelmann4 \((R_{734}-R_{747})/(R_{715}+R_{720})\) Vogelmann et al. (1993)

Vogelmann3 \(D_{715}/D_{705}\) Vogelmann et al. (1993)

Vogelmann \(R_{740}/R_{720}\) Vogelmann et al. (1993)

NPCI \((R_{680}-R_{430})/(R_{680}+R_{430})\)

D2 \(D_{705}/D_{722}\)

Datt3 \(D_{754}/D_{704}\)

PWI \(R_{900}/R_{970}\)

SR7 \(R_{440}/R_{690}\)

SRPI \(R_{430}/R_{680}\)

Dxxx: First derivation of reflectance values at wavelength ‘xxx’. Rxxx: Reflectance at wavelength ‘xxx’.

Reference: ?hsdar::vegindex()

Combined

Version Author Date
ac809cf pat-s 2020-03-03

ALE plots

ALE plots via package {iml}

P2 HR

Grid size: 100

Top ten HR features from permutation Vimp

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Grid size: 20

Top ten HR features from permutation Vimp

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Version Author Date
e9f4589 pat-s 2020-04-19
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P3 VI

Grid size: 100

Top ten VI features from permutation Vimp

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Version Author Date
e9f4589 pat-s 2020-04-19
ae6228a pat-s 2020-04-19
a0b0e88 pat-s 2020-04-19
4e59d28 pat-s 2020-04-19
1c8753c pat-s 2020-04-18
1e06eb1 pat-s 2020-04-18
049f6e9 pat-s 2020-04-18
f374d46 pat-s 2020-03-22

Grid size: 20

Top ten HR features from permutation Vimp

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will replace the existing scale.
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will replace the existing scale.
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will replace the existing scale.
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Version Author Date
e9f4589 pat-s 2020-04-19
ae6228a pat-s 2020-04-19
a0b0e88 pat-s 2020-04-19
4e59d28 pat-s 2020-04-19
1c8753c pat-s 2020-04-18
1e06eb1 pat-s 2020-04-18
869a536 pat-s 2020-04-18
049f6e9 pat-s 2020-04-18
f374d46 pat-s 2020-03-22

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-3.6.1-j25wr6zcofibs2zfjwg37357rjj26lqb/rlib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidyselect_0.2.5 iml_0.10.0       patchwork_1.0.0  ggpmisc_0.3.3   
 [5] ggpubr_0.1.6     magrittr_1.5     dplyr_0.8.3      hsdar_0.5.2     
 [9] caret_6.0-81     ggplot2_3.3.0    lattice_0.20-38  signal_0.7-6    
[13] rootSolve_1.7    rgdal_1.4-8      raster_3.0-12    sp_1.3-1        
[17] drake_7.12.0    

loaded via a namespace (and not attached):
  [1] colorspace_1.4-0   smoof_1.5.1        class_7.3-15      
  [4] rprojroot_1.3-2    fs_1.4.1           listenv_0.8.0     
  [7] ParamHelpers_1.12  ggrepel_0.8.0      prodlim_2018.04.18
 [10] fansi_0.4.1        lubridate_1.7.4    codetools_0.2-16  
 [13] splines_3.6.1      knitr_1.23         mco_1.0-15.1      
 [16] jsonlite_1.6       workflowr_1.6.1    Metrics_0.1.4     
 [19] kernlab_0.9-27     mlrMBO_1.1.2       compiler_3.6.1    
 [22] httr_1.4.0         backports_1.1.5    assertthat_0.2.1  
 [25] Matrix_1.2-15      lazyeval_0.2.1     cli_2.0.2         
 [28] later_1.0.0        htmltools_0.3.6    prettyunits_1.0.2 
 [31] tools_3.6.1        igraph_1.2.4.1     misc3d_0.8-4      
 [34] gtable_0.3.0       glue_1.4.0         reshape2_1.4.3    
 [37] fastmatch_1.1-0    Rcpp_1.0.3         parallelMap_1.4   
 [40] vctrs_0.2.4        RJSONIO_1.3-1.1    nlme_3.1-142      
 [43] iterators_1.0.10   timeDate_3043.102  gower_0.1.2       
 [46] xfun_0.5           mlr_2.17.0.9001    stringr_1.4.0     
 [49] globals_0.12.5     lifecycle_0.1.0    future_1.16.0     
 [52] MASS_7.3-51.4      scales_1.0.0       ipred_0.9-8       
 [55] hms_0.5.3          promises_1.0.1     parallel_3.6.1    
 [58] plot3D_1.1.1       RColorBrewer_1.1-2 BBmisc_1.11       
 [61] yaml_2.2.0         gridExtra_2.3      DiceKriging_1.5.6 
 [64] rpart_4.1-13       stringi_1.3.1      foreach_1.4.4     
 [67] checkmate_2.0.0    lhs_1.0.1          filelock_1.0.2    
 [70] lava_1.6.5         storr_1.2.1        rlang_0.4.5       
 [73] pkgconfig_2.0.3    evaluate_0.13      purrr_0.3.4       
 [76] prediction_0.3.14  labeling_0.3       recipes_0.1.4     
 [79] htmlwidgets_1.3    plyr_1.8.4         R6_2.4.1          
 [82] generics_0.0.2     base64url_1.4      txtq_0.1.4        
 [85] pillar_1.4.3       whisker_0.3-2      withr_2.1.2       
 [88] survival_2.43-3    nnet_7.3-12        future.apply_1.4.0
 [91] tibble_2.1.3       crayon_1.3.4       utf8_1.1.4        
 [94] plotly_4.8.0       rmarkdown_1.13     progress_1.2.0    
 [97] grid_3.6.1         data.table_1.12.8  git2r_0.26.1      
[100] ModelMetrics_1.2.2 digest_0.6.25      tidyr_1.0.0       
[103] httpuv_1.4.5.1     stats4_3.6.1       munsell_0.5.0     
[106] viridisLite_0.3.0