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Merge pull request #3211 from JoshuaPloshay/develop
Add the down_scale function to pecan.
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| ##' @title North America Downscale Function | ||
| ##' @name NA_downscale | ||
| ##' @author Joshua Ploshay | ||
| ##' | ||
| ##' @param data In quotes, file path for .rds containing ensemble data. | ||
| ##' @param focus_year In quotes, if SDA site run, format is yyyy/mm/dd, if NEON, yyyy-mm-dd. Restricted to years within file supplied to 'data'. | ||
| ##' @param C_pool In quotes, carbon pool of interest. Name must match carbon pool name found within file supplied to 'data'. | ||
| ##' @param covariates In quotes, file path of SpatRaster stack, used as predictors in randomForest. Layers within stack should be named. | ||
| ##' @param cords In quotes, file path for .csv file containing the site coordinates, columns named "lon" and "lat". | ||
| ##' @details This function will downscale forecast data to unmodeled locations using covariates and site locations | ||
| ##' | ||
| ##' @description This function uses the randomForest model. | ||
| ##' | ||
| ##' @return It returns the `downscale_output` list containing lists for the training and testing data sets, models, and predicted maps for each ensemble member. | ||
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| NA_downscale <- function(data, cords, covariates, focus_year, C_pool){ | ||
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| # Read in the covariates and set CRS to EPSG:4326 | ||
| covariates <- terra::rast(covariates) # ADD package to every function | ||
| terra::crs(covariates) <- "EPSG:4326" | ||
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| # Read the input data and site coordinates | ||
| input_data <- readRDS(data) | ||
| site_coordinates <- terra::vect(readr::read_csv(cords), geom=c("lon", "lat"), crs="EPSG:4326") | ||
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| # Extract the carbon data for the specified focus year | ||
| index <- which(names(input_data) == focus_year) | ||
| data <- input_data[[index]] | ||
| carbon_data <- as.data.frame(t(data[which(names(data) == C_pool)])) | ||
| names(carbon_data) <- paste0("ensemble",seq(1:ncol(carbon_data))) | ||
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| # Extract predictors from covariates raster using site coordinates | ||
| predictors <- as.data.frame(terra::extract(covariates, site_coordinates)) | ||
| predictors <- dplyr::select(predictors, -1) | ||
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| # Combine each ensemble member with all predictors | ||
| ensembles <- list() | ||
| for (i in seq_along(carbon_data)) { | ||
| ensembles[[i]] <- cbind(carbon_data[[i]], predictors) | ||
| } | ||
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| # Rename the carbon_data column for each ensemble member | ||
| for (i in 1:length(ensembles)) { | ||
| ensembles[[i]] <- dplyr::rename(ensembles[[i]], "carbon_data" = "carbon_data[[i]]") | ||
| } | ||
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| # Split the observations in each data frame into two data frames based on the proportion of 3/4 | ||
| ensembles <- lapply(ensembles, function(df) { | ||
| sample <- sample(1:nrow(df), size = round(0.75*nrow(df))) | ||
| train <- df[sample, ] | ||
| test <- df[-sample, ] | ||
| split_list <- list(train, test) | ||
| return(split_list) | ||
| }) | ||
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| # Rename the training and testing data frames for each ensemble member | ||
| for (i in 1:length(ensembles)) { | ||
| # names(ensembles) <- paste0("ensemble",seq(1:length(ensembles))) | ||
| names(ensembles[[i]]) <- c("training", "testing") | ||
| } | ||
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| # Train a random forest model for each ensemble member using the training data | ||
| output <- list() | ||
| for (i in 1:length(ensembles)) { | ||
| output[[i]] <- randomForest::randomForest(ensembles[[i]][[1]][["carbon_data"]] ~ land_cover+tavg+prec+srad+vapr+nitrogen+phh2o+soc+sand, | ||
| data = ensembles[[i]][[1]], | ||
| ntree = 1000, | ||
| na.action = stats::na.omit, | ||
| keep.forest = T, | ||
| importance = T) | ||
| } | ||
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| # Generate predictions (maps) for each ensemble member using the trained models | ||
| maps <- list(ncol(output)) | ||
| for (i in 1:length(output)) { | ||
| maps[[i]] <- terra::predict(object = covariates, | ||
| model = output[[i]],na.rm = T) | ||
| } | ||
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| # Organize the results into a single output list | ||
| downscale_output <- list(ensembles, output, maps) | ||
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| # Rename each element of the output list with appropriate ensemble numbers | ||
| for (i in 1:length(downscale_output)) { | ||
| names(downscale_output[[i]]) <- paste0("ensemble",seq(1:length(downscale_output[[i]]))) | ||
| } | ||
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| # Rename the main components of the output list | ||
| names(downscale_output) <- c("data", "models", "maps") | ||
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| return(downscale_output) | ||
| } |
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