estimate-methods {spcosa} | R Documentation |
Methods for estimating statistics given a spatial sample.
statistic
: spatial mean
, spatial variance
, sampling variance
, standard error
, or scdf
. See the examples below for details.statistic
: spatial mean
, sampling variance
, or standard error
."SamplingVariance"
for more details."StandardError"
for more details."SamplingPatternRandomSamplingUnits"
for more details."SpatialMean"
for more details."SpatialVariance"
for more details.
## Not run: # read vector representation of the "Mijdrecht" area (the Netherlands) shp <- readOGR(dsn = system.file("maps", package = "spcosa"), layer = "mijdrecht") # stratify into 30 strata (set nTry to a lower value to speed-up computation) myStratification <- stratify(shp, nStrata = 30, nTry = 10, verbose = TRUE) # random sampling of two sampling units per stratum mySamplingPattern <- spsample(myStratification, n = 2) # plot sampling pattern plot(myStratification, mySamplingPattern) # simulate data (in real world cases these data have to be obtained by field work) myData <- as(mySamplingPattern, "data.frame") myData$observation <- rnorm(n = nrow(myData), mean = 10, sd = 1) # design-based inference estimate("spatial mean", myStratification, mySamplingPattern, myData["observation"]) estimate("sampling variance", myStratification, mySamplingPattern, myData["observation"]) estimate("standard error", myStratification, mySamplingPattern, myData["observation"]) estimate("spatial variance", myStratification, mySamplingPattern, myData["observation"]) estimate("scdf", myStratification, mySamplingPattern, myData["observation"]) ## End(Not run)