| validateMap { RStoolbox} | R Documentation |
Map accuracy assessment
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, nSamplesV = 500, mode = "classification", classMapping = NULL )
Arguments
map |
RasterLayer. The classified map. |
valData |
sf or sp object with validation data (POLYGONs or POINTs). |
responseCol |
Character. Column containing the validation data in attribute table of |
nSamplesV |
Integer. Number of pixels to sample for validation (only applies to polygons). |
mode |
Character. Either 'classification' or 'regression'. |
classMapping |
optional data.frame with columns |
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)
[Package RStoolbox version 0.3.0 Index]