Description

validate a map from a classification or regression model. This can be useful to update the accuracy assessment after filtering, e.g. for a minimum mapping unit.

Usage

validateMap(map, valData, responseCol, nSamples = 500,
  mode = "classification", classMapping = NULL)

Arguments

map	RasterLayer. The classified map.
valData	SpatialPolygonsDataFrame or SpatialPointsDataFrame with validation data.
responseCol	Character. Column containing the validation data in attribute table of valData.
nSamples	Integer. Number of pixels to sample for validation (only applies to polygons).
mode	Character. Either 'classification' or 'regression'.
classMapping	optional data.frame with columns 'class' and 'classID' defining the mapping from raster integers to class names.

Note

Polygons, which are smaller than the map resolution will only be considered if they overlap with a pixel center coordinate, otherwise they will be ignored.

Examples

## Not run: 
## library(caret)
## library(raster)
## 
## ## Training data
## data(lsat)
## poly     <- readRDS(system.file("external/trainingPolygons.rds", package="RStoolbox"))
## 
## ## Split training data in training and validation set (50%-50%)
## splitIn   <- createDataPartition(poly$class, p = .5)[[1]]
## train <- poly[splitIn,]
## val   <- poly[-splitIn,]
## 
## ## Classify (deliberately poorly)
## sc <- superClass(lsat, trainData = train, responseCol = "class", nSamples = 50, model = "mlc")
## 
## ## Polish map with majority filter
## 
## polishedMap <- focal(sc$map, matrix(1,3,3), fun = modal) 
## 
## ## Validation
## ## Before filtering
## val0 <- validateMap(sc$map, valData = val, responseCol = "class", 
##                             classMapping = sc$classMapping)
## ## After filtering
## val1 <- validateMap(polishedMap, valData = val, responseCol = "class",
##                              classMapping = sc$classMapping)
## End(Not run)

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