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This document shows the prediction maps for the possible infection risk of trees in the Basque Country by the following pathogens:
The following algorithms were used to create the predictions:
Unfortunately, XGBOOST cannot handle new factor levels in prediction data. Since we predict to the whole Basque Country using environment variables, this case occurs quite often. Variables like “soil type” and “lithology type” inherit instances which only occur in some parts of the prediction area but not within the training data. Therefore, it was not possible to create prediction maps for XGBOOST.
[1] NA
[1] NA
[1] NA
[1] NA
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-9.2.0/openblas-0.3.7-epeitvjwewaa3avb3brxkgbim4rh3qwb/lib/libopenblas_zen-r0.3.7.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tidyselect_0.2.5 workflowr_1.3.0 here_0.1
[4] kableExtra_1.1.0 ggExtra_0.8 ggrepel_0.8.0
[7] reporttools_1.1.2 xtable_1.8-3 cowplot_0.9.3
[10] hrbrthemes_0.6.0 ggpubr_0.2 future.callr_0.4.0
[13] furrr_0.1.0.9002 future_1.11.1.1 ggsci_2.9
[16] clustermq_0.8.8 ggspatial_1.0.3 ggplot2_3.0.0
[19] rgenoud_5.8-3.0 fs_1.2.6 curl_3.2
[22] R.utils_2.7.0 R.oo_1.22.0 R.methodsS3_1.7.1
[25] GSIF_0.5-5 stringr_1.3.1 RSAGA_1.3.0
[28] plyr_1.8.4 shapefiles_0.7 foreign_0.8-71
[31] gstat_1.1-6 glue_1.3.0 rasterVis_0.45
[34] latticeExtra_0.6-28 RColorBrewer_1.1-2 lattice_0.20-35
[37] raster_2.8-19 viridis_0.5.1 viridisLite_0.3.0
[40] rgdal_1.4-3 sp_1.3-1 tibble_2.0.1
[43] forcats_0.3.0 lwgeom_0.1-6 dplyr_0.8.0.1
[46] sf_0.7-4 parallelMap_1.3 purrr_0.2.5
[49] mlrMBO_1.1.2 smoof_1.5.1 checkmate_1.8.5
[52] BBmisc_1.11 magrittr_1.5 mlr_2.13.9000
[55] ParamHelpers_1.11 drake_7.7.0
loaded via a namespace (and not attached):
[1] backports_1.1.2 Hmisc_4.2-0 fastmatch_1.1-0
[4] igraph_1.2.2 lazyeval_0.2.1 splines_3.5.1
[7] storr_1.2.1 listenv_0.7.0 digest_0.6.15
[10] htmltools_0.3.6 base64url_1.4 cluster_2.0.7-1
[13] readr_1.3.1 globals_0.12.4 extrafont_0.17
[16] xts_0.11-0 extrafontdb_1.0 colorspace_1.3-2
[19] rvest_0.3.2 pixmap_0.4-11 xfun_0.7
[22] DiceKriging_1.5.6 callr_3.1.0 crayon_1.3.4
[25] jsonlite_1.5 hexbin_1.27.2 survival_2.42-3
[28] zoo_1.8-3 gtable_0.2.0 webshot_0.5.1
[31] Rttf2pt1_1.3.7 abind_1.4-5 scales_1.0.0
[34] DBI_1.0.0 miniUI_0.1.1.1 Rcpp_1.0.0
[37] plotrix_3.7-4 spData_0.2.9.0 htmlTable_1.12
[40] units_0.6-2 txtq_0.2.0 Formula_1.2-3
[43] intervals_0.15.1 dismo_1.1-4 htmlwidgets_1.3
[46] httr_1.3.1 FNN_1.1 aqp_1.17
[49] acepack_1.4.1 pkgconfig_2.0.2 reshape_0.8.8
[52] XML_3.98-1.16 nnet_7.3-12 RJSONIO_1.3-1.1
[55] labeling_0.3 later_0.7.5 rlang_0.3.1
[58] munsell_0.5.0 tools_3.5.1 evaluate_0.13
[61] yaml_2.2.0 processx_3.2.1 knitr_1.23
[64] mime_0.5 whisker_0.3-2 xml2_1.2.0
[67] compiler_3.5.1 rstudioapi_0.10 png_0.1-7
[70] prettymapr_0.2.2 plotly_4.8.0 filelock_1.0.2
[73] e1071_1.7-0 spacetime_1.2-2 lhs_0.16
[76] stringi_1.2.4 ps_1.2.1 gdtools_0.1.7
[79] plot3D_1.1.1 Matrix_1.2-14 classInt_0.2-3
[82] rosm_0.2.2 pillar_1.3.1 plotKML_0.5-9
[85] data.table_1.11.8 httpuv_1.4.5 colorRamps_2.3
[88] R6_2.2.2 promises_1.0.1 gridExtra_2.3
[91] codetools_0.2-15 MASS_7.3-50 assertthat_0.2.0
[94] rprojroot_1.3-2 withr_2.1.2 hms_0.4.2
[97] parallel_3.5.1 grid_3.5.1 rpart_4.1-13
[100] tidyr_0.8.2 class_7.3-14 rmarkdown_1.12
[103] misc3d_0.8-4 mco_1.0-15.1 git2r_0.23.0
[106] shiny_1.2.0 base64enc_0.1-3