tests/testthat/test-biomod.R

In biomodhub/biomod2: Ensemble Platform for Species Distribution Modeling

# library(readr)
# library(dplyr)
# library(rlang)
# # library(biomod2)
# # devtools::load_all(".")
# 
# # species occurrences
# species.dat <-
#   read_csv(
#     system.file("external/species/mammals_table.csv", package="biomod2")
#   )
# 
# head(species.dat)
# 
# # the name of studied species
# resp.name <- 'GuloGulo'
# 
# # the presence/absences data for our species
# resp.var <- species.dat %>% pull(resp.name)
# 
# # the XY coordinates of species data
# resp.xy <- species.dat %>% select_at(c('X_WGS84', 'Y_WGS84'))
# 
# 
# # Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
# expl.name <- paste0('bio', c(3, 4, 7, 11, 12), '.grd')
# expl.var <-
#   purrr::map(
#     expl.name,
#     ~ raster::raster(
#       system.file(file.path('external/bioclim/current', .x), package="biomod2"),
#       rat = FALSE)
#   ) %>%
#   raster::stack()
# 
# # 1. Formatting Data
# bm.formdat <-
#   BIOMOD_FormatingData(
#     resp.var = resp.var,
#     expl.var = expl.var,
#     resp.xy = resp.xy,
#     resp.name = resp.name
#   )
# 
# # 2. Defining Models Options using default options.
# bm.opt <- BIOMOD_ModelingOptions()
# 
# # 3. Doing Modelisation
# bm.mod <-
#   BIOMOD_Modeling(
#     bm.formdat,
#     models = c('SRE','RF', 'MAXENT.Phillips.2'),
#     models.options = bm.opt,
#     NbRunEval = 2,
#     DataSplit = 80,
#     VarImport = 0,
#     models.eval.meth = c('TSS','ROC'),
#     do.full.models = FALSE,
#     modeling.id = "test"
#   )
# 
# ## print a summary of modeling stuff
# bm.mod
# 
# # 4.1 Projection on current environemental conditions
# 
# bm.proj <-
#   BIOMOD_Projection(
#     modeling.output = bm.mod,
#     new.env = expl.var,
#     proj.name = 'current',
#     selected.models = 'all',
#     binary.meth = 'TSS',
#     compress = FALSE,
#     build.clamping.mask = FALSE
#   )
# 
# 
# # bm.maxent.mod.list <-
# #   BIOMOD_LoadModels(bm.mod, models='MAXENT.Phillips.2')
# # 
# # bm.maxent.mod.1 <- get(bm.maxent.mod.list[1])
# # 
# # bm.maxent.proj.1a <- predict(bm.maxent.mod.1, newdata = expl.var)
#