dissever: Spatial downscaling
In pierreroudier/dissever: Spatial Downscaling using the Dissever Algorithm

Description Usage Arguments Author(s) References Examples

Performs spatial downscaling of coarse grid mapping to fine grid mapping using predictive covariates and a model fitted using the caret package.

## S4 method for signature 'RasterLayer,RasterLayer'
dissever(coarse, fine, method = "rf",
  p = 0.5, nmax = NULL, thresh = 0.01, min_iter = 5, max_iter = 20,
  boot = NULL, level = 0.9, tune_length = 3,
  tune_grid = .create_tune_grid(model = method, tune_length = tune_length),
  train_control_init = .default_control_init,
  train_control_iter = .default_control_iter, verbose = FALSE)

`coarse`	object of class `"RasterLayer"`, the coarse-resolution layer that needs to be downscaled
`fine`	object of class `"RasterStack"`, the fine-resolution stack of predictive covariates
`method`	a string specifying which classification or regression model to use (via the caret package). Possible values are found using names(caret::getModelInfo()).
`p`	numeric, proportion of the fine map that is sampled for fitting the dissever model (between 0 and 1, defaults to 0.5)
`nmax`	numeric maximum number of pixels selected for fitting the dissever model. It will override the number of pixels chosen by the `p` option if that number is over the value passed to `nmax`.
`thresh`	numeric, dissever iterations will proceed until the RMSE of the dissever model reaches this value, or until the maximum number of iterations is met (defaults to 0.01)
`min_iter`	numeric, minimum number of iterations (defaults to 5)
`max_iter`	numeric, maximum number of iterations (defaults to 20)
`boot`	numeric, if not NULL (default), the number of bootstrap replicates used to derive the confidence intervals.
`level`	If this is a numeric value, it is used to derive confidence intervals using quantiles. If it is a function, it is used to derive the uncertainty using this function.
`tune_length`	numeric, the number of parameters to test to find the optimal parametrisation of the caret model (defaults to 3)
`tune_grid`	a data frame with possible tuning values
`train_control_init`	Control parameters for finding the optimal parameters of the caret model (see trainControl)
`train_control_iter`	Control parameters for fitting the caret model during the iteration phase (see trainControl)
`verbose`	controls the verbosity of the output (TRUE or FALSE)

Brendan Malone, Pierre Roudier

Malone, B.P, McBratney, A.B., Minasny, B., Wheeler, I., (2011) A general method for downscaling earth resource information. Computers & Geosciences, 41: 119-125. http://dx.doi.org/10.1016/j.cageo.2011.08.021

# Load the Edgeroi dataset (see ?edgeroi)
data(edgeroi)

# Plot the Edgeroi dataset (using the raster package)
library(raster)
plot(edgeroi$carbon) # coarse resolution layer
plot(edgeroi$predictors) # fine resolution predictors

# Run dissever using a simple linear model.

# In this instance we are subsampling heavily (p = 0.05) to keep
# run time short
res_lm <- dissever(
  coarse = edgeroi$carbon,
  fine = edgeroi$predictors,
  method = "lm",
  min_iter = 5, max_iter = 10,
  p = 0.05
)

# A lot more models are available through caret:
## Not run: 
subset(caret::modelLookup(), forReg == TRUE, select = 'model')

## End(Not run)

# Plot dissever results
plot(res_lm, type = 'map', main = "Dissever using GAM")
plot(res_lm, type = 'perf', main = "Dissever using GAM")