scale_prediction_rasters: scale_prediction_rasters

In mrecos/klrfome: Kernel Logistic Regression with Focal Mean Embeddings

scale_prediction_rasters

Description

Center and scale the prediction raster stack to the parameters of the training data used to fit a model

Usage

scale_prediction_rasters(pred_var_stack, params, verbose = 1)

Arguments

pred_var_stack	[raster stack] stack of input landscape rasters for prediction
params	[list] list of mean and standard deviation parameters returned from formatting data. (See example below)
verbose	[integer] a value of '1' to print out the landscape variable name as it is being normalized.

Details

'scale_prediction_rasters()' - Function scales your predictor rater stack based on the params list created in the model fitting process. This script simply loops over the rasters in the stack and centers and scales based on mean and sd of the training data used to fit the klr model. The function outputs a raster stack.

Value

[raster stack] a stack of the prediction rasters scaled to the the input training parameters.

Examples

## Not run: 
### Create param list
params <- list(train_data = train_data,
alphas_pred = train_log_pred[["alphas"]],
sigma = sigma,
lambda = lambda,
means = formatted_data$means,
sds = formatted_data$sds)

### width and hieght of roving focal window (required)
ngb = 5
### Number of rows and columns in prediction rasters
## needed for making simulated rasters, as well as for predicting real-world rasters
cols = 100
rows = 100

### Create simulated environmental rasters  (sim data only) ####
s_var1r <- NLMR::nlm_gaussianfield(cols,rows, autocorr_range = 20)
s_var1 <- rescale_sim_raster(s_var1r, 50, 10) 
s_var2 <- rescale_sim_raster(s_var1r, 3, 2) 
b_var1r <- NLMR::nlm_gaussianfield(cols,rows,autocorr_range = 20)
b_var1 <- rescale_sim_raster(b_var1r, 100, 20) 
b_var2 <- rescale_sim_raster(b_var1r, 6, 3) 
### Create a site-present trend surface  (sim data only)
trend_coords <- sim_trend(cols, rows, n = 3)
coords <- trend_coords$coords
trend <- trend_coords$trend
inv_trend <- abs(1-trend)
var1 <- (s_var1 * trend) + (b_var1 * inv_trend)
var2 <- (s_var2 * trend) + (b_var2 * inv_trend)
#### end simulated data creation ####

### Create raster stack of predictor variables
pred_var_stack <- raster::stack(var1, var2)
names(pred_var_stack) <- c("var1","var2")
### scale rasters to training data
pred_var_stack_scaled <- scale_prediction_rasters(pred_var_stack, params, verbose = 0)
### Predict raster (single chunk, not in parallel) 
pred_rast <- KLR_raster_predict(pred_var_stack_scaled, ngb = ngb, params, split = FALSE, ppside = NULL,
                                progress = FALSE, parallel = FALSE)

## End(Not run)

mrecos/klrfome documentation built on April 6, 2022, 8:02 p.m.