Merge pull request #42 from adamlilith/solstice_2022_2023

Add `trainESM()`

R/trainESM.r

@@ -140,6 +140,8 @@ trainESM <- function(
 
 	}
 
-	list(models = models, tuning = tuning)
+	out <- list(models = models, tuning = tuning)
+	if (is.na(scale) || scale) out$scale <- scales
+	out
 
 }

README.md

@@ -41,7 +41,7 @@ You may need to install the `remotes` package first.
 ### Model training ###
 * `trainByCrossValid` and `summaryByCrossValid`: Calibrate a distribution/niche model using cross-validation
 * `trainBRT`: Boosted regression trees (BRTs)
-* `trainESM`: Ensembles of small models (ESM)
+* `trainESM`: Ensembles of small models (ESMs)
 * `trainGAM`: Generalized additive models (GAMs)
 * `trainGLM`: Generalized linear models (GLMs)
 * `trainMaxEnt`: MaxEnt models

man/examples/trainESM_examples.r

@@ -2,8 +2,6 @@
 # designed to work fast, not produce accurate, defensible models. They can
 # take a few minutes to run.
 
-library(mgcv)
-library(sf)
 library(terra)
 set.seed(123)
 
@@ -79,3 +77,25 @@ esm2 <- trainESM(
 
 str(esm2, 1)
 esm2$tuning
+
+### make a set of predictions to rasters
+########################################
+
+# center environmental rasters and divide by their SD
+madClimScaled <- scale(madClim, center = esm2$scale$mean, scale = esm2$scale$sd)
+
+# make one raster per model
+predictions <- list()
+for (i in 1:length(esm2$models)) {
+    predictions[[i]] <- predict(madClimScaled, esm2$models[[i]], type = 'response')
+}
+
+# combine into a "stack"
+predictions <- do.call(c, predictions)
+names(predictions) <- esm2$tuning$model
+plot(predictions)
+
+# calculate (unweighted) mean
+prediction <- mean(predictions)
+plot(prediction)
+plot(occs, pch = 1, add = TRUE)