Support for terra's SpatRaster objects

DESCRIPTION

@@ -18,6 +18,6 @@ Depends: R (>= 3.5.0)
 Imports: caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr, zoo, methods, grDevices, data.table, lattice
 Suggests: doParallel, randomForest, lubridate, raster, sp, knitr, mapview,
     rmarkdown, sf, scales, parallel, latticeExtra, virtualspecies, gridExtra, 
-    viridis, rgeos, stars, scam
-RoxygenNote: 7.1.0
+    viridis, rgeos, stars, scam, terra
+RoxygenNote: 7.1.1
 VignetteBuilder: knitr

R/aoa.R

@@ -8,7 +8,7 @@
 #' variable importance of the machine learning algorithm used for model training.
 #' The AOA is derived by applying a threshold on the DI which is the (outlier-removed)
 #' maximum DI of the cross-validated training data.
-#' @param newdata A RasterStack, RasterBrick or data.frame containing the data
+#' @param newdata A RasterStack, RasterBrick, SpatRaster or data.frame containing the data
 #' the model was meant to make predictions for.
 #' @param model A train object created with caret used to extract weights from (based on variable importance) as well as cross-validation folds
 #' @param cl A cluster object e.g. created with doParallel. Should only be used if newdata is large.
@@ -119,12 +119,20 @@ aoa <- function(newdata,
     }
   }
   as_stars <- FALSE
+  as_terra <- FALSE
   if (inherits(newdata, "stars")) {
     if (!requireNamespace("stars", quietly = TRUE))
       stop("package stars required: install that first")
     newdata = methods::as(newdata, "Raster")
     as_stars <- TRUE
   }
+
+  if (inherits(newdata, "SpatRaster")) {
+    if (!requireNamespace("terra", quietly = TRUE))
+      stop("package terra required: install that first")
+    newdata = methods::as(newdata, "Raster")
+    as_terra <- TRUE
+  }
   #### Prepare output as either as RasterLayer or vector:
   out <- NA
   if (inherits(newdata, "Raster"))
@@ -299,6 +307,8 @@ aoa <- function(newdata,
     out <- raster::stack(out,AOA)
     if (as_stars)
       out <- split(stars::st_as_stars(out), "band")
+    if(as_terra)
+      out = methods::as(out, "SpatRaster")
   }else{
     out <- DI_out
     AOA <- rep(1,length(out))

R/calibrate_aoa.R

@@ -63,6 +63,7 @@
 calibrate_aoa <- function(AOA,model, window.size=5, calib="scam",multiCV=FALSE,
                           length.out = 10, maskAOA=TRUE, showPlot=TRUE,k=6,m=2){
   as_stars <- FALSE
+  as_terra <- FALSE
   if (inherits(AOA, "stars")) {
     if (!requireNamespace("stars", quietly = TRUE))
       stop("package stars required: install that first")
@@ -72,6 +73,15 @@ calibrate_aoa <- function(AOA,model, window.size=5, calib="scam",multiCV=FALSE,
     as_stars <- TRUE
   }
 
+  if (inherits(AOA, "SpatRaster")) {
+    if (!requireNamespace("terra", quietly = TRUE))
+      stop("package terra required: install that first")
+    attr <- attributes(AOA)[c("aoa_stats","TrainDI")]
+    AOA <- methods::as(AOA, "Raster")
+    attributes(AOA)<- c(attributes(AOA),attr)
+    as_terra <- TRUE
+  }
+
   if(multiCV){
     preds_all <- data.frame()
     train_predictors <- model$trainingData[,-which(names(model$trainingData)==".outcome")]
@@ -274,6 +284,11 @@ calibrate_aoa <- function(AOA,model, window.size=5, calib="scam",multiCV=FALSE,
     AOA <- split(stars::st_as_stars(AOA), "band")
     attributes(AOA)<- c(attributes(AOA),attr)
   }
+
+  if(as_terra){
+    AOA <- methods::as(AOA, "SpatRaster")
+    attributes(AOA)<- c(attributes(AOA),attr)
+  }
   names(AOA)[names(AOA)=="expectedError"] <- paste0("expected_",model$metric)
   #return(AOA)
 

vignettes/terra-showcase.Rmd

@@ -0,0 +1,71 @@
+---
+title: "Showcase with terra SpatRaster"
+date: "`r Sys.Date()`"
+output:
+  rmarkdown::html_document:
+    toc: true
+    theme: united
+vignette: >
+  %\VignetteIndexEntry{Showcase with terra SpatRaster}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r setup, echo=FALSE}
+knitr::opts_chunk$set(fig.width = 8.83)
+```
+
+---
+
+# Introduction
+
+This vignette is meant to showcase that the CAST package now is able to handle
+terra SpatRaster objects. Changes to the code were implemented only in the `aoa()` and
+`calibrate_aoa()` function and are restricted to coercing a SpatRaster input
+to a RasterStack as well as coercing the output back to SpatRaster.
+Below is a minimal example based on the AOA-tutorial vignette.
+
+```{r  message = FALSE, warning=FALSE}
+library(CAST)
+library(terra)
+library(raster)
+library(lubridate)
+library(methods)
+```
+
+```{r}
+predictors_sp <- stack(system.file("extdata","predictors_2012-03-25.grd",package="CAST"))
+data <- get(load(system.file("extdata","Cookfarm.RData",package="CAST")))
+trainDat <- data[data$altitude==-0.3&
+                   year(data$Date)==2012&
+                   week(data$Date)%in%c(10:12),]
+
+library(caret)
+predictors <- c("DEM","TWI","Precip_cum","cday",
+                "MaxT_wrcc","Precip_wrcc","BLD",
+                "Northing","Easting","NDRE.M")
+set.seed(10)
+model <- train(trainDat[,predictors],trainDat$VW,
+               method="rf",tuneGrid=data.frame("mtry"=2),
+               importance=TRUE,ntree=50,
+               trControl=trainControl(method="cv",number=3))
+```
+
+From here we will coerce the Raster object to SpatRaster objects to show
+that the aoa and calibrate_aoa functions work on SpatRasters as well. The 
+application might not make sense on a thematic level. The example was drafted
+just to show that the functions accept and return SpatRaster objects.
+
+```{r}
+predictors_terra = as(predictors_sp, "SpatRaster")
+AOA <- aoa(predictors_terra, model)
+class(AOA)
+attributes(AOA)$aoa_stats
+```
+
+```{r}
+AOA_calib <- calibrate_aoa(AOA,model,window.size = 5,length.out = 5, multiCV=TRUE,showPlot=FALSE)
+class(AOA_calib$AOA)
+AOA_calib$plot
+```
+