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SDM-3_tuningdata.R
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SDM-3_tuningdata.R
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# Make data frame for tuning hyperparameters by spatially-stratified CV
library(tidyverse)
library(terra)
library(sf)
pastEAland <- rast("./data/climate/pastEAland.tif")
names(pastEAland) <- make.names(names(pastEAland), allow_ = FALSE)
load("SDM-2_CVfolds.RData")
## Full data frame for P.s.
saPS <- mask(pastEAland, vect(st_union(st_buffer(psEA, dist = 200e3))))
PS <- as.data.frame(saPS, xy = TRUE, cells = TRUE)
PS <- PS %>%
mutate(fold = case_when(x > quintilexPS[1] & x < quintilexPS[2] ~ 1,
x > quintilexPS[2] & x < quintilexPS[3] ~ 2,
x > quintilexPS[3] & x < quintilexPS[4] ~ 3,
x > quintilexPS[4] & x < quintilexPS[5] ~ 4,
x > quintilexPS[5] & x < quintilexPS[6] ~ 5))
filterfactors <- 2^(seq.int(0,7))
meshes <- filterfactors %>%
map(~ aggregate(pastEAland$CHELSA.bio1.1981.2010.V.2.1, fact = .)) %>%
set_names(filterfactors)
psEA <- psEA %>%
mutate(cellnr = cells(saPS, vect(psEA))[,2],
mesh1nr = cells(meshes$`1`, vect(psEA))[,2],
mesh2nr = cells(meshes$`2`, vect(psEA))[,2],
mesh4nr = cells(meshes$`4`, vect(psEA))[,2],
mesh8nr = cells(meshes$`8`, vect(psEA))[,2],
mesh16nr = cells(meshes$`16`, vect(psEA))[,2],
mesh32nr = cells(meshes$`32`, vect(psEA))[,2],
mesh64nr = cells(meshes$`64`, vect(psEA))[,2],
mesh128nr = cells(meshes$`128`, vect(psEA))[,2])
psEA <- group_by(psEA, fold)
presmesh1cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh1nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh2cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh2nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh4cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh4nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh8cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh8nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh16cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh16nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh32cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh32nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh64cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh64nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh128cells <- group_map(psEA, function(x, ...) {
distinct(x, mesh128nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
PS <- mutate(PS,
presmesh1 = if_else(cell %in% presmesh1cells, 1, 0),
presmesh2 = if_else(cell %in% presmesh2cells, 1, 0),
presmesh4 = if_else(cell %in% presmesh4cells, 1, 0),
presmesh8 = if_else(cell %in% presmesh8cells, 1, 0),
presmesh16 = if_else(cell %in% presmesh16cells, 1, 0),
presmesh32 = if_else(cell %in% presmesh32cells, 1, 0),
presmesh64 = if_else(cell %in% presmesh64cells, 1, 0),
presmesh128 = if_else(cell %in% presmesh128cells, 1, 0))
summary(PS)
PS <- select(PS, -cell, -x, -y)
## Full data frame for T.h.
saTH <- mask(pastEAland, vect(st_union(st_buffer(thEA, dist = 200e3))))
TH <- as.data.frame(saTH, xy = TRUE, cells = TRUE)
TH <- TH %>%
mutate(fold = case_when(x > quintilexTH[1] & x < quintilexTH[2] ~ 1,
x > quintilexTH[2] & x < quintilexTH[3] ~ 2,
x > quintilexTH[3] & x < quintilexTH[4] ~ 3,
x > quintilexTH[4] & x < quintilexTH[5] ~ 4,
x > quintilexTH[5] & x < quintilexTH[6] ~ 5))
filterfactors <- 2^(seq.int(0,7))
meshes <- filterfactors %>%
map(~ aggregate(pastEAland$CHELSA.bio1.1981.2010.V.2.1, fact = .)) %>%
set_names(filterfactors)
thEA <- thEA %>%
mutate(cellnr = cells(saTH, vect(thEA))[,2],
mesh1nr = cells(meshes$`1`, vect(thEA))[,2],
mesh2nr = cells(meshes$`2`, vect(thEA))[,2],
mesh4nr = cells(meshes$`4`, vect(thEA))[,2],
mesh8nr = cells(meshes$`8`, vect(thEA))[,2],
mesh16nr = cells(meshes$`16`, vect(thEA))[,2],
mesh32nr = cells(meshes$`32`, vect(thEA))[,2],
mesh64nr = cells(meshes$`64`, vect(thEA))[,2],
mesh128nr = cells(meshes$`128`, vect(thEA))[,2])
thEA <- group_by(thEA, fold)
presmesh1cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh1nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh2cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh2nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh4cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh4nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh8cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh8nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh16cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh16nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh32cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh32nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh64cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh64nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
presmesh128cells <- group_map(thEA, function(x, ...) {
distinct(x, mesh128nr, .keep_all = TRUE) %>%
pull(cellnr)
} ) %>%
unlist()
TH <- mutate(TH,
presmesh1 = if_else(cell %in% presmesh1cells, 1, 0),
presmesh2 = if_else(cell %in% presmesh2cells, 1, 0),
presmesh4 = if_else(cell %in% presmesh4cells, 1, 0),
presmesh8 = if_else(cell %in% presmesh8cells, 1, 0),
presmesh16 = if_else(cell %in% presmesh16cells, 1, 0),
presmesh32 = if_else(cell %in% presmesh32cells, 1, 0),
presmesh64 = if_else(cell %in% presmesh64cells, 1, 0),
presmesh128 = if_else(cell %in% presmesh128cells, 1, 0))
summary(TH)
TH <- select(TH, -cell, -x, -y)
## Combine species
df <- bind_rows(mutate(PS, species = 'ps'), mutate(TH, species = 'th'))
save(df, file = "SDM-3_tuningdata.RData")
sessionInfo()
# R version 4.1.3 (2022-03-10)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 19044)
#
# Matrix products: default
#
# locale:
# [1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252
# [3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
# [5] LC_TIME=English_United Kingdom.1252
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] sf_1.0-8 terra_1.6-17 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.10 purrr_0.3.4 readr_2.1.2
# [8] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.1
#
# loaded via a namespace (and not attached):
# [1] tidyselect_1.1.2 haven_2.5.0 colorspace_2.0-3 vctrs_0.4.2 generics_0.1.3
# [6] utf8_1.2.2 rlang_1.0.6 e1071_1.7-11 pillar_1.8.1 glue_1.6.2
# [11] withr_2.5.0 DBI_1.1.3 dbplyr_2.1.1 modelr_0.1.8 readxl_1.4.0
# [16] lifecycle_1.0.3 munsell_0.5.0 gtable_0.3.1 cellranger_1.1.0 rvest_1.0.2
# [21] codetools_0.2-18 tzdb_0.3.0 class_7.3-20 fansi_1.0.3 broom_0.8.0
# [26] Rcpp_1.0.9 KernSmooth_2.23-20 scales_1.2.1 backports_1.4.1 classInt_0.4-7
# [31] jsonlite_1.8.2 fs_1.5.2 hms_1.1.1 stringi_1.7.6 grid_4.1.3
# [36] cli_3.4.1 tools_4.1.3 magrittr_2.0.3 proxy_0.4-27 crayon_1.5.1
# [41] pkgconfig_2.0.3 ellipsis_0.3.2 xml2_1.3.3 reprex_2.0.1 lubridate_1.8.0
# [46] assertthat_0.2.1 httr_1.4.3 rstudioapi_0.13 R6_2.5.1 units_0.8-0
# [51] compiler_4.1.3