#' Takes an emtools.species object with presence and background points, and builds a random forest model using the 'probability mode' in package `ranger`
#'
#' @param species An enmtools.species object
#' @param env A SpatRaster of environmental data.
#' @param f A formula for fitting the model
#' @param test.prop Proportion of data to withhold randomly for model evaluation, or "block" for spatially structured evaluation.
#' @param eval Determines whether model evaluation should be done. Turned on by default, but moses turns it off to speed things up.
#' @param nback Number of background points to draw from range or env, if background points aren't provided
#' @param env.nback Number of points to draw from environment space for environment space discrimination metrics.
#' @param report Optional name of an html file for generating reports
#' @param overwrite TRUE/FALSE whether to overwrite a report file if it already exists
#' @param rts.reps The number of replicates to do for a Raes and ter Steege-style test of significance
#' @param bg.source Source for drawing background points. If "points", it just uses the background points that are already in the species object. If "range", it uses the range raster. If "env", it draws points at randome from the entire study area outlined by the first environmental layer.
#' @param verbose Controls printing of various messages progress reports. Defaults to FALSE.
#' @param clamp When set to TRUE, clamps the environmental layers so that predictions made outside the min/max of the training data for each predictor are set to the value for the min/max for that predictor. Prevents the model from extrapolating beyond the min/max bounds of the predictor space the model was trained in, although there could still be projections outside the multivariate training space if predictors are strongly correlated.
#' @param corner An integer from 1 to 4. Selects which corner to use for "block" test data. By default the corner is selected randomly.
#' @param bias An optional raster estimating relative sampling effort per grid cell. Will be used for drawing background data.
#' @param ... Arguments to be passed to \code{\link[ranger]{ranger}}
#'
#' @return An enmtools model object containing species name, model formula (if any), model object, suitability raster, marginal response plots, and any evaluation objects that were created.
#'
#' @examples
#' \donttest{
#' enmtools.rf.ranger(iberolacerta.clade$species$monticola, env = euro.worldclim, nback = 500)
#' }
enmtools.rf.ranger <- function(species, env, f = NULL, test.prop = 0, eval = TRUE, nback = 1000, env.nback = 10000, report = NULL, overwrite = FALSE, rts.reps = 0, bg.source = "default", verbose = FALSE, clamp = TRUE, corner = NA, bias = NA, ...){
notes <- NULL
env <- check.raster(env, "env")
species <- check.bg(species, env, nback = nback, bg.source = bg.source, verbose = verbose, bias = bias)
# Builds a default formula using all env
if(is.null(f)){
f <- as.formula(paste("presence", paste(c(names(env)), collapse = " + "), sep = " ~ "))
notes <- c(notes, "No formula was provided, so a formula was built automatically.")
}
rf.ranger.precheck(f, species, env)
# Declaring some NAs in case we skip evaluation
test.data <- NA
model.evaluation <- NA
env.model.evaluation <- NA
test.evaluation <- NA
env.test.evaluation <- NA
rts.test <- NA
# Code for randomly withheld test data
if(is.numeric(test.prop)){
if(test.prop > 0 & test.prop < 1){
test.inds <- sample(1:nrow(species$presence.points), ceiling(nrow(species$presence.points) * test.prop))
test.data <- species$presence.points[test.inds,]
species$presence.points <- species$presence.points[-test.inds,]
}
}
# Code for spatially structured test data
if(is.character(test.prop)){
if(test.prop == "block"){
if(is.na(corner)){
corner <- ceiling(runif(1, 0, 4))
} else if(corner < 1 | corner > 4){
stop("corner should be an integer from 1 to 4!")
}
test.inds <- get.block(terra::crds(species$presence.points), terra::crds(species$background.points))
test.bg.inds <- which(test.inds$bg.grp == corner)
test.inds <- which(test.inds$occ.grp == corner)
test.data <- species$presence.points[test.inds,]
test.bg <- species$background.points[test.bg.inds,]
species$presence.points <- species$presence.points[-test.inds,]
species$background.points <- species$background.points[-test.bg.inds,]
}
}
### Add env data
species <- add.env(species, env, verbose = verbose)
# Recast this formula so that the response variable is named "presence"
# regardless of what was passed.
f <- reformulate(attr(delete.response(terms(f)), "term.labels"), response = "presence")
analysis.df <- make_analysis.df(species)
analysis.df$presence <- as.factor(analysis.df$presence)
this.rf <- ranger::ranger(f, analysis.df[,-c(1,2)], probability = TRUE, ...)
pfun <- function(model, data, ...) {
predict(model, data, ...)$predictions[ , 2]
}
suitability <- terra::predict(env, this.rf, fun = pfun, type = "response", na.rm = TRUE)
# Clamping and getting a diff layer
clamping.strength <- NA
if(clamp == TRUE){
env <- clamp.env(analysis.df, env)
clamped.suitability <- terra::predict(env, this.rf, fun = pfun, type = "response", na.rm = TRUE)
clamping.strength <- clamped.suitability - suitability
suitability <- clamped.suitability
}
if(eval == TRUE){
# This is a very weird hack that has to be done because dismo's evaluate function
# fails if the stack only has one layer.
if(length(names(env)) == 1){
oldname <- names(env)
env <- c(env, env)
names(env) <- c(oldname, "dummyvar")
notes <- c(notes, "Only one predictor was provided, so a dummy variable was created in order to be compatible with dismo's prediction function.")
}
model.evaluation <- dismo::evaluate(predict(this.rf, data = analysis.df[analysis.df$presence == 1, ])$predictions[ , 2, drop = TRUE],
predict(this.rf, data = analysis.df[analysis.df$presence == 0, ])$predictions[ , 2, drop = TRUE])
env.model.evaluation <- env.evaluate(species, this.rf, env, n.background = env.nback)
# Test eval for randomly withheld data
if(is.numeric(test.prop)){
if(test.prop > 0 & test.prop < 1){
test.check <- terra::extract(env, test.data, ID = FALSE)
test.data <- test.data[complete.cases(test.check),]
test.evaluation <- dismo::evaluate(predict(this.rf, data = terra::extract(env, test.data, ID = FALSE))$predictions[ , 2, drop = TRUE],
predict(this.rf, data = terra::extract(env, species$background.points, ID = FALSE))$predictions[ , 2, drop = TRUE])
temp.sp <- species
temp.sp$presence.points <- test.data
env.test.evaluation <- env.evaluate(temp.sp, this.rf, env, n.background = env.nback)
}
}
# Test eval for spatially structured data
if(is.character(test.prop)){
if(test.prop == "block"){
test.check <- terra::extract(env, test.data, ID = FALSE)
test.data <- test.data[complete.cases(test.check),]
test.check <- terra::extract(env, test.bg, ID = FALSE)
test.bg <- test.bg[complete.cases(test.check),]
test.evaluation <- dismo::evaluate(predict(this.rf, data = terra::extract(env, test.data, ID = FALSE))$predictions[ , 2, drop = TRUE],
predict(this.rf, data = terra::extract(env, test.bg, ID = FALSE))$predictions[ , 2, drop = TRUE])
temp.sp <- species
temp.sp$presence.points <- test.data
temp.sp$background.points <- test.bg
env.test.evaluation <- env.evaluate(temp.sp, this.rf, env, n.background = env.nback)
}
}
# Do Raes and ter Steege test for significance. Turned off if eval == FALSE
if(rts.reps > 0 && eval == TRUE){
message("\nBuilding RTS replicate models...\n")
# Die if we're not doing randomly withheld test data and RTS reps > 0
if(!is.numeric(test.prop)){
stop(paste("RTS test can only be conducted with randomly withheld data, and test.prop is set to", test.prop))
}
rts.models <- list()
rts.geog.training <- c()
rts.geog.test <- c()
rts.env.training <- c()
rts.env.test <- c()
if (requireNamespace("progress", quietly = TRUE)) {
pb <- progress::progress_bar$new(
format = " [:bar] :percent eta: :eta",
total = rts.reps, clear = FALSE, width= 60)
}
for(i in 1:rts.reps){
if (requireNamespace("progress", quietly = TRUE)) {
pb$tick()
}
if(verbose == TRUE){message(paste("Replicate", i, "of", rts.reps))}
# Repeating analysis with scrambled pa points and then evaluating models
rep.species <- species
# Mix the points all together
if(test.prop > 0) {
test <- as.data.frame(test.data, geom = "XY")[ , c("x", "y")]
} else {
test <- NULL
}
allpoints <- rbind(test,
as.data.frame(species$background.points, geom = "XY")[ , c("x", "y")],
as.data.frame(species$presence.points, geom = "XY")[ , c("x", "y")])
# Sample presence points from pool and remove from pool
rep.rows <- sample(nrow(allpoints), nrow(species$presence.points))
rep.species$presence.points <- terra::vect(allpoints[rep.rows,], geom=c("x", "y"), crs = terra::crs(species$presence.points))
allpoints <- allpoints[-rep.rows,]
# Do the same for test points
if(test.prop > 0){
test.rows <- sample(nrow(allpoints), nrow(test.data))
rep.test.data <- allpoints[test.rows,]
allpoints <- allpoints[-test.rows,]
}
# Everything else goes back to the background
rep.species$background.points <- terra::vect(allpoints, geom=c("x", "y"), crs = terra::crs(species$presence.points))
rep.species <- add.env(rep.species, env, verbose = verbose)
rts.df <- make_analysis.df(rep.species)
rts.df$presence <- as.factor(rts.df$presence)
rts.df <- rts.df[complete.cases(rts.df), ]
thisrep.rf <- ranger::ranger(f, rts.df[,-c(1,2)], probability = TRUE, ...)
thisrep.model.evaluation <- dismo::evaluate(predict(thisrep.rf, data = rts.df[rts.df$presence == 1, ])$predictions[ , 2, drop = TRUE],
predict(thisrep.rf, data = rts.df[rts.df$presence == 0, ])$predictions[ , 2, drop = TRUE])
thisrep.env.model.evaluation <- env.evaluate(rep.species, thisrep.rf, env, n.background = env.nback)
rts.geog.training[i] <- thisrep.model.evaluation@auc
rts.env.training[i] <- thisrep.env.model.evaluation@auc
if(test.prop > 0 & test.prop < 1){
temp.sp <- rep.species
temp.sp$presence.points <- terra::vect(rep.test.data, geom=c("x", "y"), crs = terra::crs(species$presence.points))
temp.sp <- add.env(temp.sp, env, verbose = verbose)
rep.test.data2 <- make_analysis.df(temp.sp)
rep.test.data2$presence <- as.factor(rep.test.data2$presence)
rep.test.data2 <- rep.test.data2[complete.cases(rep.test.data2), ]
thisrep.test.evaluation <-dismo::evaluate(predict(thisrep.rf, data = rep.test.data2)$predictions[ , 2, drop = TRUE],
predict(thisrep.rf, data = rts.df[rts.df$presence == 0, ])$predictions[ , 2, drop = TRUE])
thisrep.env.test.evaluation <- env.evaluate(temp.sp, thisrep.rf, env, n.background = env.nback)
rts.geog.test[i] <- thisrep.test.evaluation@auc
rts.env.test[i] <- thisrep.env.test.evaluation@auc
rts.models[[paste0("rep.",i)]] <- list(model = thisrep.rf,
training.evaluation = thisrep.model.evaluation,
env.training.evaluation = thisrep.env.model.evaluation,
test.evaluation = thisrep.test.evaluation,
env.test.evaluation = thisrep.env.test.evaluation)
} else {
rts.models[[paste0("rep.",i)]] <- list(model = thisrep.rf,
training.evaluation = thisrep.model.evaluation,
env.training.evaluation = thisrep.env.model.evaluation,
test.evaluation = NA,
env.test.evaluation = NA)
}
}
# Reps are all run now, time to package it all up
# Calculating p values
rts.geog.training.pvalue = mean(rts.geog.training > model.evaluation@auc)
rts.env.training.pvalue = mean(rts.env.training > env.model.evaluation@auc)
if(test.prop > 0){
rts.geog.test.pvalue <- mean(rts.geog.test > test.evaluation@auc)
rts.env.test.pvalue <- mean(rts.env.test > env.test.evaluation@auc)
} else {
rts.geog.test.pvalue <- NA
rts.env.test.pvalue <- NA
}
rts.geog.training <- data.frame(AUC = rts.geog.training)
rts.env.training <- data.frame(AUC = rts.env.training)
rts.geog.test <- data.frame(AUC = rts.geog.test)
rts.env.test <- data.frame(AUC = rts.env.test)
# Making plots
training.plot <- ggplot(rts.geog.training, aes(x = .data$AUC, fill = "density", alpha = 0.5)) +
geom_histogram(binwidth = 0.05) +
geom_vline(xintercept = model.evaluation@auc, linetype = "longdash") +
xlim(-0.05,1.05) + guides(fill = "none", alpha = "none") + xlab("AUC") +
ggtitle(paste("Model performance in geographic space on training data")) +
theme(plot.title = element_text(hjust = 0.5))
env.training.plot <- ggplot(rts.env.training, aes(x = .data$AUC, fill = "density", alpha = 0.5)) +
geom_histogram(binwidth = 0.05) +
geom_vline(xintercept = env.model.evaluation@auc, linetype = "longdash") +
xlim(-0.05,1.05) + guides(fill = "none", alpha = "none") + xlab("AUC") +
ggtitle(paste("Model performance in environment space on training data")) +
theme(plot.title = element_text(hjust = 0.5))
# Make plots for test AUC distributions
if(test.prop > 0){
test.plot <- ggplot(rts.geog.test, aes(x = .data$AUC, fill = "density", alpha = 0.5)) +
geom_histogram(binwidth = 0.05) +
geom_vline(xintercept = test.evaluation@auc, linetype = "longdash") +
xlim(-0.05,1.05) + guides(fill = "none", alpha = "none") + xlab("AUC") +
ggtitle(paste("Model performance in geographic space on test data")) +
theme(plot.title = element_text(hjust = 0.5))
env.test.plot <- ggplot(rts.env.test, aes(x = .data$AUC, fill = "density", alpha = 0.5)) +
geom_histogram(binwidth = 0.05) +
geom_vline(xintercept = env.test.evaluation@auc, linetype = "longdash") +
xlim(-0.05,1.05) + guides(fill = "none", alpha = "none") + xlab("AUC") +
ggtitle(paste("Model performance in environment space on test data")) +
theme(plot.title = element_text(hjust = 0.5))
} else {
test.plot <- NA
env.test.plot <- NA
}
rts.pvalues = list(rts.geog.training.pvalue = rts.geog.training.pvalue,
rts.env.training.pvalue = rts.env.training.pvalue,
rts.geog.test.pvalue = rts.geog.test.pvalue,
rts.env.test.pvalue = rts.env.test.pvalue)
rts.distributions = list(rts.geog.training = rts.geog.training,
rts.env.training = rts.env.training,
rts.geog.test = rts.geog.test,
rts.env.test = rts.env.test)
rts.plots = list(geog.training.plot = training.plot,
env.training.plot = env.training.plot,
geog.test.plot = test.plot,
env.test.plot = env.test.plot)
rts.test <- list(rts.models = rts.models,
rts.pvalues = rts.pvalues,
rts.distributions = rts.distributions,
rts.plots = rts.plots,
rts.nreps = rts.reps)
}
}
output <- list(species.name = species$species.name,
formula = f,
analysis.df = analysis.df,
test.data = test.data,
test.prop = test.prop,
model = this.rf,
training.evaluation = model.evaluation,
test.evaluation = test.evaluation,
env.training.evaluation = env.model.evaluation,
env.test.evaluation = env.test.evaluation,
rts.test = rts.test,
suitability = suitability,
clamping.strength = clamping.strength,
call = sys.call(),
notes = notes)
class(output) <- c("enmtools.rf.ranger", "enmtools.model")
# Doing response plots for each variable. Doing this bit after creating
# the output object because marginal.plots expects an enmtools.model object
response.plots <- list()
plot.vars <- all.vars(f)
for(i in 2:length(plot.vars)){
this.var <-plot.vars[i]
if(this.var %in% names(env)){
response.plots[[this.var]] <- marginal.plots(output, env, this.var)
}
}
output[["response.plots"]] <- response.plots
if(!is.null(report)){
if(file.exists(report) & overwrite == FALSE){
stop("Report file exists, and overwrite is set to FALSE!")
} else {
# message("\n\nGenerating html report...\n")
message("This function not enabled yet. Check back soon!")
# makereport(output, outfile = report)
}
}
return(output)
}
# Summary for objects of class enmtools.rf
summary.enmtools.rf.ranger <- function(object, plot = TRUE, ...){
cat("\n\nFormula: ")
cat(deparse(object$formula))
cat("\n\nData table (top ten lines): ")
print(kable(head(object$analysis.df, 10)))
cat("\n\nModel: ")
print(summary(object$model))
cat("\n\nModel fit (training data): ")
print(object$training.evaluation)
cat("\n\nEnvironment space model fit (training data): ")
print(object$env.training.evaluation)
cat("\n\nProportion of data wittheld for model fitting: ")
cat(object$test.prop)
cat("\n\nModel fit (test data): ")
print(object$test.evaluation)
cat("\n\nEnvironment space model fit (test data): ")
print(object$env.test.evaluation)
cat("\n\nSuitability: \n")
print(object$suitability)
cat("\n\nNotes: \n")
object$notes
if(plot) {
plot(object)
}
}
# Print method for objects of class enmtools.rf
print.enmtools.rf.ranger <- function(x, ...){
print(summary(x, ...))
}
# Plot method for objects of class enmtools.rf
plot.enmtools.rf.ranger <- function(x, ...){
suit.points <- data.frame(rasterToPoints2(x$suitability))
colnames(suit.points) <- c("x", "y", "Suitability")
test <- terra::as.data.frame(x$test.data, geom = "XY")
suit.plot <- ggplot(data = suit.points, aes(y = .data$y, x = .data$x)) +
geom_raster(aes(fill = .data$Suitability)) +
scale_fill_viridis_c(option = "B", guide = guide_colourbar(title = "Suitability")) +
coord_fixed() + theme_classic() +
geom_point(data = x$analysis.df[x$analysis.df$presence == 1,], aes(y = .data$y, x = .data$x),
pch = 21, fill = "white", color = "black", size = 2)
if(inherits(x$test.data, "SpatVector")){
suit.plot <- suit.plot + geom_point(data = test, aes(y = .data$y, x = .data$x),
pch = 21, fill = "green", color = "black", size = 2)
}
if(!is.na(x$species.name)){
title <- paste("Random forest model for", x$species.name)
suit.plot <- suit.plot + ggtitle(title) + theme(plot.title = element_text(hjust = 0.5))
}
return(suit.plot)
}
# Predict method for models of class enmtools.rf.ranger
predict.enmtools.rf.ranger <- function(object, env, maxpts = 1000, clamp = TRUE, ...){
pfun <- function(object, data, ...) {
predict(object, data, ...)$predictions[ , 2]
}
# Make a plot of habitat suitability in the new region
suitability <- terra::predict(env, object$model, fun = pfun, type = "response", na.rm = TRUE)
# Clamping and getting a diff layer
clamping.strength <- NA
if(clamp == TRUE){
env <- clamp.env(object$analysis.df, env)
clamped.suitability <- terra::predict(env, object$model, fun = pfun, type = "response", na.rm = TRUE)
clamping.strength <- clamped.suitability - suitability
suitability <- clamped.suitability
}
suit.points <- data.frame(rasterToPoints2(suitability))
colnames(suit.points) <- c("x", "y", "Suitability")
suit.plot <- ggplot(data = suit.points, aes(y = .data$y, x = .data$x)) +
geom_raster(aes(fill = .data$Suitability)) +
scale_fill_viridis_c(option = "B", guide = guide_colourbar(title = "Suitability")) +
coord_fixed() + theme_classic()
clamp.points <- data.frame(rasterToPoints2(clamping.strength))
colnames(clamp.points) <- c("x", "y", "Clamping")
clamp.plot <- ggplot(data = clamp.points, aes(y = .data$y, x = .data$x)) +
geom_raster(aes_string(fill = "Clamping")) +
scale_fill_viridis_c(option = "B", guide = guide_colourbar(title = "Suitability")) +
coord_fixed() + theme_classic()
if(!is.na(object$species.name)){
title <- paste("Ranger random forests model projection for", object$species.name)
suit.plot <- suit.plot + ggtitle(title) + theme(plot.title = element_text(hjust = 0.5))
}
this.threespace = threespace.plot(object, env, maxpts)
output <- list(suitability.plot = suit.plot,
suitability = suitability,
clamping.strength = clamping.strength,
clamp.plot = clamp.plot,
threespace.plot = this.threespace)
return(output)
}
# Function for checking data prior to running enmtools.rf
rf.ranger.precheck <- function(f, species, env){
# Check to see if the function is the right class
if(!inherits(f, "formula")){
stop("Argument \'formula\' must contain an R formula object!")
}
### Check to make sure the data we need is there
if(!inherits(species, "enmtools.species")){
stop("Argument \'species\' must contain an enmtools.species object!")
}
check.species(species)
if(!inherits(species$presence.points, "SpatVector")){
stop("Species presence.points do not appear to be an object of class SpatVector")
}
if(!inherits(species$background.points, "SpatVector")){
stop("Species background.points do not appear to be an object of class SpatVector")
}
if(!inherits(env, c("SpatRaster"))){
stop("No environmental rasters were supplied!")
}
}
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