RStoolbox (version 0.1.6)

validateMap: 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, 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.

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$class, p = .5)[[1]]
# train <- poly[splitIn,]
# val   <- poly[-splitIn,]
# 
# ## Classify (deliberately poorly)
# sc <- superClass(lsat, trainData = train, responseCol = "class", nSamples = 50, model = "mlc")
# 
# ## Polish map with majority filter
# 
# polishMap <- focal(sc$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(polishMap, valData = val, responseCol = "class", classMapping = sc$classMapping)
# ## End(Not run)