prep_polygons()
dsmartr_class_maps
dir.create
params for nested pathsdsmartr_iterate
dsmartr_check_polygons
dsmartr_prep_polygons
dsmartr_pred_mask
, which creates masking rasters for areas where predictions maybe shouldn't be made, on a per-iteration basis.dsmartr_unstack
, flattened the list structure of the output because nested lists are super annoying. Also tweaked the output folder name for term consistency. Naming things is hard.dsmartr_prepare
, added an option to set a ceiling for area_proportional sampling. This may prevent oversampling very large polygons that have been given an unrealistically simple composition.dsmartr_collate
does.dsmartr_collate
to pick a random soil where a tie for n-most-likely exists. Previously, ties were output in (essentially) alphabetical order, which is stupid.dsmartr_eval_ties
, which maps out pixels where more than one soil type is tied
for most-likely.dsmartr_iterate
now writes only the points that were actually used in the model where write_samples = TRUE
, instead of all the points that were generated by iter_sample_poly
dsmartr_collate
, a better approach to environment assignments for clustered processingdsmartr_iterate
, split out some code into separate internal functions. One, dsmartr_get_classes
makes sure that soil classes are assigned consistent values in all output rasters, including cases where known sites are used, and those known sites are of soil classes that have not been mapped. The second, iter_sample_poly
subsamples the output of dsmartr_prep_*
on each model iteration. Separating the function makes it easier to modify and test.options(stringsAsFactors = FALSE)
to make your day-to-day munging scripts work, you're going to have a bad time turning them into a package... bugfixesAdd the following code to your website.
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