spcosa (version 0.2-2)

estimate-methods: Estimating Statistics

Description

Methods for estimating statistics given a spatial sample.

Examples

# 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"])