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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          1.06        724          69     1
 2 B70          0.884       729.         70     2
 3 B71          0.635       734.         71     3
 4 B68          0.610       719.         68     4
 5 B118         0.546       957.        118     5
 6 B115         0.499       942.        115     6
 7 B109         0.473       914         109     7
 8 B117         0.443       952         117     8
 9 B113         0.436       933         113     9
10 B114         0.401       938.        114    10
# … with 112 more rows
# A tibble: 90 x 3
   feature    importance  rank
   <chr>           <dbl> <int>
 1 Vogelmann2      1.93      1
 2 Vogelmann4      1.64      2
 3 Vogelmann       1.09      3
 4 D2              0.726     4
 5 Vogelmann3      0.687     5
 6 Carter          0.591     6
 7 Gitelson2       0.516     7
 8 Boochs2         0.504     8
 9 Carter5         0.445     9
10 NPCI            0.396    10
# … with 80 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

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
6f2584e pat-s 2021-02-24
0c235c9 pat-s 2020-05-06
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28f8e7a pat-s 2020-05-03
e9f4589 pat-s 2020-04-19
630ad21 pat-s 2020-04-19
1e06eb1 pat-s 2020-04-18
869a536 pat-s 2020-04-18
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7df7c8e pat-s 2020-04-18
16366b0 pat-s 2020-03-05
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0c15797 pat-s 2020-03-04
ac809cf pat-s 2020-03-03

Plots by dataset

HR

P2 Absolute permutation based Var Imp

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
77810ea pat-s 2020-05-06
28f8e7a pat-s 2020-05-03
1e06eb1 pat-s 2020-04-18
869a536 pat-s 2020-04-18
049f6e9 pat-s 2020-04-18
7df7c8e pat-s 2020-04-18
ac809cf pat-s 2020-03-03

VI

P3 Absolute permutation based Var Imp

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
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
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
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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replace the existing scale.
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replace the existing scale.
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replace the existing scale.

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
6f2584e pat-s 2021-02-24
77810ea pat-s 2020-05-06
28f8e7a pat-s 2020-05-03
b00f87d pat-s 2020-04-20
0268923 pat-s 2020-04-20
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

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
6f2584e pat-s 2021-02-24
b00f87d pat-s 2020-04-20
0268923 pat-s 2020-04-20
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
72b9228 pat-s 2020-03-17
e252447 pat-s 2020-03-17

P3 VI

Grid size: 100

Top ten VI features from permutation Vimp

Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
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replace the existing scale.
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replace the existing scale.
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replace the existing scale.
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replace the existing scale.
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replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
6f2584e pat-s 2021-02-24
b00f87d pat-s 2020-04-20
0268923 pat-s 2020-04-20
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

Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
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replace the existing scale.
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replace the existing scale.
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replace the existing scale.
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replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.

Version Author Date
1b8710f pat-s 2021-04-05
29bc678 pat-s 2021-03-31
6f2584e pat-s 2021-02-24
b00f87d pat-s 2020-04-20
0268923 pat-s 2020-04-20
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 4.0.4 (2021-02-15)
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-4.0.4-udi7a3ahhtokdcoyqdbndhebeupt7hid/rlib/R/lib/libRblas.so
LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/r-4.0.4-udi7a3ahhtokdcoyqdbndhebeupt7hid/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 datasets  utils     methods   base     

other attached packages:
 [1] iml_0.10.1        patchwork_1.1.1   ggpmisc_0.3.8-1   ggpubr_0.4.0     
 [5] dplyr_1.0.4       hsdar_0.5.2       caret_6.0-86      ggplot2_3.3.3    
 [9] lattice_0.20-41   signal_0.7-6      rootSolve_1.8.2.1 rgdal_1.5-23     
[13] raster_3.4-5      sp_1.4-5          usethis_2.0.0     magrittr_2.0.1   
[17] drake_7.13.2     

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         mlr_2.19.0.9000      backports_1.2.1     
  [4] fastmatch_1.1-0      workflowr_1.6.2      plyr_1.8.6          
  [7] igraph_1.2.6         lazyeval_0.2.2       splines_4.0.4       
 [10] storr_1.2.5          listenv_0.8.0        digest_0.6.27       
 [13] foreach_1.5.1        htmltools_0.5.1.1    fansi_0.4.2         
 [16] checkmate_2.0.0      BBmisc_1.11          base64url_1.4       
 [19] openxlsx_4.2.3       Metrics_0.1.4        recipes_0.1.15      
 [22] globals_0.14.0       gower_0.2.2          prettyunits_1.1.1   
 [25] colorspace_2.0-0     ggrepel_0.9.1        haven_2.3.1         
 [28] xfun_0.20            DiceKriging_1.5.8    tcltk_4.0.4         
 [31] crayon_1.4.0         jsonlite_1.7.2       survival_3.2-7      
 [34] iterators_1.0.13     glue_1.4.2           gtable_0.3.0        
 [37] ipred_0.9-9          kernlab_0.9-29       car_3.0-10          
 [40] future.apply_1.7.0   abind_1.4-5          scales_1.1.1        
 [43] smoof_1.6.0.2        rstatix_0.6.0        Rcpp_1.0.6          
 [46] viridisLite_0.3.0    progress_1.2.2       foreign_0.8-81      
 [49] txtq_0.2.3           stats4_4.0.4         prediction_0.3.14   
 [52] lava_1.6.8.1         prodlim_2019.11.13   htmlwidgets_1.5.3   
 [55] httr_1.4.2           RColorBrewer_1.1-2   ellipsis_0.3.1      
 [58] farver_2.0.3         pkgconfig_2.0.3      ParamHelpers_1.14   
 [61] nnet_7.3-15          utf8_1.1.4           RJSONIO_1.3-1.4     
 [64] labeling_0.4.2       tidyselect_1.1.0     rlang_0.4.10        
 [67] reshape2_1.4.4       later_1.1.0.1        munsell_0.5.0       
 [70] cellranger_1.1.0     tools_4.0.4          cli_2.4.0           
 [73] generics_0.1.0       broom_0.7.4          evaluate_0.14       
 [76] stringr_1.4.0        yaml_2.2.1           ModelMetrics_1.2.2.2
 [79] knitr_1.31           fs_1.5.0             zip_2.1.1           
 [82] purrr_0.3.4          future_1.21.0        nlme_3.1-152        
 [85] whisker_0.4          compiler_4.0.4       rstudioapi_0.13     
 [88] plotly_4.9.3         filelock_1.0.2       curl_4.3            
 [91] ggsignif_0.6.0       lhs_1.1.1            tibble_3.0.6        
 [94] stringi_1.5.3        highr_0.8            forcats_0.5.1       
 [97] plot3D_1.3           Matrix_1.3-2         vctrs_0.3.6         
[100] pillar_1.4.7         lifecycle_0.2.0      data.table_1.13.6   
[103] httpuv_1.5.5         R6_2.5.0             promises_1.1.1      
[106] renv_0.13.2          rio_0.5.16           parallelly_1.23.0   
[109] codetools_0.2-18     MASS_7.3-53          rprojroot_2.0.2     
[112] withr_2.4.1          parallel_4.0.4       hms_1.0.0           
[115] grid_4.0.4           rpart_4.1-15         timeDate_3043.102   
[118] tidyr_1.1.2          class_7.3-18         rmarkdown_2.6       
[121] misc3d_0.9-0         mco_1.15.6           carData_3.0-4       
[124] git2r_0.28.0         parallelMap_1.5.0    pROC_1.17.0.1       
[127] mlrMBO_1.1.5         lubridate_1.7.9.2