`biomod2` - Modeling `r sp.name` - report
In biomod2: Ensemble Platform for Species Distribution Modeling

knitr::opts_chunk$set(echo = TRUE, comment = NA)
options(width = 100)
library(biomod2)
library(terra)
library(data.table)
library(kableExtra)
library(DT)
library(patchwork)

bm.calib = NULL
hasEvalMod = hasEvalEns = FALSE
hasVarImpMod = hasVarImpEns = FALSE
hasBmFiles = !is.na(bm.files)
hasBmMod = !is.na(bm.mod)
hasBmEns = !is.na(bm.ens)
hasBmForm = !is.na(bm.form)
ind.mod = ind.ens = ind.proj = NA
color1 = params.color$color1
color2 = params.color$color2
color3 = params.color$color3

bm.calib = get_calib_lines(bm.mod)

hasPA = inherits(bm.form, "BIOMOD.formated.data.PA")
hasDataEval = ifelse("has.data.eval" %in% slotNames(bm.form), bm.form@has.data.eval, FALSE)
isBinary = (bm.form@data.type == "binary")

# form.summ = summary(bm.form, calib.lines = bm.calib) ## TODO CORRECT
form.summ = data.frame("dataset" = "initial",
                       "run" = NA,
                       "PA" = NA,
                       "Presences" = length(which(bm.form@data.species == 1)),
                       "True_Absences" = length(which(bm.form@data.species == 0)),
                       "Pseudo_Absences" = 0,
                       "Undefined" = length(which(is.na(bm.form@data.species))))

nb.1 = form.summ$Presences[which(form.summ$dataset == "initial")]
nb.0 = form.summ$True_Absences[which(form.summ$dataset == "initial")]
nb.NA = form.summ$Pseudo_Absences[which(form.summ$dataset == "calibration" &
                                          form.summ$run == "allRun" &
                                          form.summ$PA == "PA1")]
nb.points = length(bm.form@data.species)

# ind.mod = which(bm.files$file == sub("[.]/", "", bm.mod@link))
ind.mod = which(bm.files$file == sub(paste0(".*", sp.name, "/"), paste0(sp.name, "/"), bm.mod@link))
ind.proj = which(bm.files$type == "projection" &
                   bm.files$refer_to == bm.files$refer_to[ind.mod])
name.proj = unique(bm.files$ID[ind.proj])

ind.ens = which(bm.files$type == "models" &
                        bm.files$level == "ensemble" &
                        bm.files$refer_to == bm.files$refer_to[ind.mod])

eval.mod = get_evaluations(bm.mod)
hasEvalMod = !is.null(eval.mod)

varImp.mod = get_variables_importance(bm.mod)
hasVarImpMod = !is.null(varImp.mod)
if (hasVarImpMod) {
  varImp.mod$var.imp = round(varImp.mod$var.imp, 3)
}

eval.ens = get_evaluations(bm.ens)
hasEvalEns = !is.null(eval.ens)

varImp.ens = get_variables_importance(bm.ens)
hasVarImpEns = !is.null(varImp.ens)
if (hasVarImpEns) {
  varImp.ens$var.imp = round(varImp.ens$var.imp, 3)
}

# name.var = get_formal_data(bm.mod, subinfo = "expl.var.names")
name.models.mod = get_built_models(bm.mod)
name.algo = sapply(name.models.mod, function(x) strsplit(x, "_")[[1]][4])
name.algo = sort(unique(name.algo))

name.models.ens = get_built_models(bm.ens)
name.ens = sapply(name.models.ens, function(x) strsplit(x, "_")[[1]][2])
name.ens = sort(unique(name.ens))

tab = knitr::kable(bm.files[, c("file", "path", "type", "level", "ID")], format = "html")
tab = kable_styling(tab, bootstrap_options = "condensed")
if (any(!is.na(c(ind.mod, ind.ens)))) {
  row_spec(tab, c(ind.mod, ind.ens), bold = TRUE, color = color1)
}

Formated data

The r sp.name species dataset contains r nb.points points.

cat('The', sp.name, ' species dataset contains ', nb.1, 'presences and ', nb.0, ' true absences.')

cat('
Pseudo-absences have been drawn from the'
, form.summ$Undefined[which(form.summ$dataset == "initial")]
, ' available background points.
')

print(bm.form)

tab = knitr::kable(form.summ, format = "html")
kable_styling(tab, bootstrap_options = "hover")

form.plot = plot(bm.form, calib.lines = bm.calib,
                 plot.type = ifelse(bm.form@data.type == "binary", "raster", "points"))

Single models

print(bm.mod)

Modeling options

print(get_options(bm.mod))

Evaluation

rmCol = c("full.name")
if (!hasDataEval) { rmCol = c("full.name", "evaluation") }
DT::datatable(eval.mod[, -which(colnames(eval.mod) %in% rmCol)])

pp1 = bm_PlotEvalMean(bm.out = bm.mod, dataset = "calibration", do.plot = FALSE)
pp2 = bm_PlotEvalMean(bm.out = bm.mod, dataset = "validation", do.plot = FALSE)
pp3 = bm_PlotEvalMean(bm.out = bm.mod, dataset = "evaluation", do.plot = FALSE)
pp1$plot + pp2$plot + pp3$plot

pp1 = bm_PlotEvalBoxplot(bm.out = bm.mod, dataset = "calibration"
                         , group.by = c("algo", "run"), do.plot = FALSE)
pp2 = bm_PlotEvalBoxplot(bm.out = bm.mod, dataset = "validation"
                         , group.by = c("algo", "run"), do.plot = FALSE)
pp3 = bm_PlotEvalBoxplot(bm.out = bm.mod, dataset = "evaluation"
                         , group.by = c("algo", "run"), do.plot = FALSE)
pp1$plot + pp2$plot + pp3$plot

Variables' importance

rmCol = c("full.name")
DT::datatable(varImp.mod[, -which(colnames(varImp.mod) %in% rmCol)])

pp = bm_PlotVarImpBoxplot(bm.out = bm.mod
                          , group.by = c("algo", "expl.var", "expl.var")
                          , do.plot = FALSE)
pp$plot

Response curves

if (length(name.models.mod) > 9) {
  # mod.ind = seq(1, length(name.models.mod), 9)
  # mod.ind = c(mod.ind, length(name.models.mod))
  # mod.ind = unique(mod.ind)
  # mod.sel = lapply(1:(length(mod.ind)-1), function(x) {
  #   name.models.mod[mod.ind[x]:mod.ind[x+1]]
  # })

  mod.sel = lapply(name.algo, function(x) {
    grep(x, name.models.mod, value = TRUE)
  })
} else {
  mod.sel = list("all")
}

for (mod.i in mod.sel) {
  pp = bm_PlotResponseCurves(bm.out = bm.mod
                             , fixed.var = "mean"
                             , models.chosen = mod.i
                             , main = ""
                             , do.bivariate = FALSE
                             , do.plot = FALSE
                             , do.progress = FALSE)
  if(bm.mod@data.type == "binary"){
    pp = pp$plot + ylim(0, 1) + guides(color = guide_legend(ncol = 3))
  } else {
    pp <- pp$plot + guides(color = guide_legend(ncol = 3))
  }
  print(pp)
}

# require(foreach)
# require(confintr)
# require(ggplot2)
# 
# # tmp = resp_models$tab
# tmp = TAB
# tmp$pred.name = as.character(tmp$pred.name)
# tmp$PA = sapply(tmp$pred.name, function(x) strsplit(x, "_")[[1]][2])
# tmp$RUN = sapply(tmp$pred.name, function(x) strsplit(x, "_")[[1]][3])
# tmp$algo = sapply(tmp$pred.name, function(x) strsplit(x, "_")[[1]][4])
# tmp.split = split(tmp, list(tmp$expl.name, tmp$algo, tmp$id))
# tmp.mean = foreach(tabi = tmp.split, .combine = "rbind") %do%
#   {
#     res = unique(tabi[, c("id", "expl.name", "algo", "expl.val")])
#     if (length(unique(tabi$pred.val)) == 1) {
#       res$pred.val = res$conf.inf = res$conf.sup = mean(tabi$pred.val)
#     } else {
#       res$pred.val = mean(tabi$pred.val)
#       res$conf.inf = ci_mean(tabi$pred.val)$interval[1]
#       res$conf.sup = ci_mean(tabi$pred.val)$interval[2]
#     }
#     return(res)
#   }
# head(tmp.mean)
# 
# ggplot(tmp.mean, aes_string(x = "expl.val", y = "pred.val", group = "algo")) + 
#   geom_ribbon(aes(ymin = conf.inf, ymax = conf.sup), alpha = 0.5, fill = "grey60") +
#   geom_line(aes(color = algo)) + 
#   facet_wrap("expl.name", scales = "free_x") + 
#   xlab("") + 
#   ylab("") + 
#   ylim(0, 1) +
#   theme(legend.title = element_blank(), 
#         legend.key = element_rect(fill = "white"), legend.position = "bottom", 
#         axis.text.x = element_text(angle = 45, hjust = 1))

Ensemble models

print(bm.ens)

Evaluation

rmCol = c("full.name")
if (!hasDataEval) { rmCol = c("full.name", "evaluation") }
tab = knitr::kable(eval.ens[, -which(colnames(eval.ens) %in% rmCol)], format = "html")
kable_styling(tab, bootstrap_options = "hover")

pp1 = bm_PlotEvalMean(bm.out = bm.ens, dataset = "calibration", do.plot = FALSE)
pp2 = bm_PlotEvalMean(bm.out = bm.ens, dataset = "validation", do.plot = FALSE)
pp3 = bm_PlotEvalMean(bm.out = bm.ens, dataset = "evaluation", do.plot = FALSE)
pp1$plot + pp2$plot + pp3$plot

pp1 = bm_PlotEvalBoxplot(bm.out = bm.ens, dataset = "calibration"
                         , group.by = c("algo", "algo"), do.plot = FALSE)
pp2 = bm_PlotEvalBoxplot(bm.out = bm.ens, dataset = "validation"
                         , group.by = c("algo", "algo"), do.plot = FALSE)
pp3 = bm_PlotEvalBoxplot(bm.out = bm.ens, dataset = "evaluation"
                         , group.by = c("algo", "algo"), do.plot = FALSE)
pp1$plot + pp2$plot + pp3$plot

Variables' importance

rmCol = c("full.name")
DT::datatable(varImp.ens[, -which(colnames(varImp.ens) %in% rmCol)])

pp = bm_PlotVarImpBoxplot(bm.out = bm.ens
                          , group.by = c("algo", "expl.var", "expl.var")
                          , do.plot = FALSE)
pp$plot

Response curves

if (any(grepl("EMcv", name.models.ens))) {
  mod.sel = list(name.models.ens[!grepl("EMcv", name.models.ens)]
                 , name.models.ens[grepl("EMcv", name.models.ens)])
} else {
  mod.sel = list("all")
}

for (mod.i in mod.sel) {
  pp = bm_PlotResponseCurves(bm.out = bm.ens
                             , fixed.var = "mean"
                             , models.chosen = mod.i
                             , main = ""
                             , do.bivariate = FALSE
                             , do.plot = FALSE
                             , do.progress = FALSE)
  pp = pp$plot + guides(color = guide_legend(ncol = 1))
  print(pp)
}

Projections

tab = knitr::kable(bm.files[, c("file", "path", "type", "level", "ID")], format = "html")
tab = kable_styling(tab, bootstrap_options = "condensed")
if (any(!is.na(ind.proj))) {
  row_spec(tab, ind.proj, bold = TRUE, color = color3)
}

for (proj.i in name.proj) {
  ind.proj.i = intersect(ind.proj, which(bm.files$ID == proj.i))
  for (j in ind.proj.i) {
    bm.proj = get(load(file = bm.files$file[j]))
    if (inherits(try(bm.proj@data.type), "try-error")) {
      bm.proj@data.type <- "binary"
    }

    print(bm.proj)
    tmp = get_predictions(bm.proj)
    isDf = inherits(tmp, "data.frame")
    if (isDf && nrow(bm.proj@coord) == 0) {
      message("/!\\ Missing coordinates. Plot not rendered.")
    } else {
      if (length(get_projected_models(bm.proj)) > 9) {
        pp = plot(bm.proj, do.plot = FALSE)
        if (isDf) {
          plot(pp + facet_wrap(~ full.name, ncol = 5))
        } else {
          plot(pp + facet_wrap(~ lyr, ncol = 5))
        }
      } else {
        plot(bm.proj)
      }
    }

    ## + clamping mask ??
  }
}
rm(list = basename(bm.files$file))

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biomod2 documentation built on Dec. 22, 2025, 5:10 p.m.

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biomod2
Ensemble Platform for Species Distribution Modeling

`biomod2` - Modeling `r sp.name` - report
In biomod2: Ensemble Platform for Species Distribution Modeling

Formated data

Single models

Modeling options

Evaluation

Variables' importance

Response curves

Ensemble models

Evaluation

Variables' importance

Response curves

Projections

Try the biomod2 package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

biomod2 Ensemble Platform for Species Distribution Modeling

`biomod2` - Modeling `r sp.name` - report In biomod2: Ensemble Platform for Species Distribution Modeling

Formated data

Single models

Modeling options

Evaluation

Variables' importance

Response curves

Ensemble models

Evaluation

Variables' importance

Response curves

Projections

Try the biomod2 package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

biomod2
Ensemble Platform for Species Distribution Modeling

`biomod2` - Modeling `r sp.name` - report
In biomod2: Ensemble Platform for Species Distribution Modeling