estimate-methods {spcosa}R Documentation

Estimating Statistics

Description

Methods for estimating statistics given a spatial sample.

Methods

statistic = "character", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame"
estimates one of the following statistics, depending on the value of argument statistic: spatial mean, spatial variance, sampling variance, standard error, or scdf. See the examples below for details.
statistic = "character", stratification = "CompactStratificationEqualArea", samplingPattern = "SamplingPatternRandomComposite", data = "data.frame"
estimates one of the following statistics, depending on the value of argument statistic: spatial mean, sampling variance, or standard error.
statistic = "SamplingVariance", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame"
estimates the sampling variance. See "SamplingVariance" for more details.
statistic = "StandardError", stratification = "CompactStratificationEqualArea", samplingPattern = "SamplingPatternRandomComposite", data = "data.frame"
estimates the standard error of the spatial mean. See "StandardError" for more details.
statistic = "SpatialCumulativeDistributionFunction", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame"
estimates the spatial cumulative distribution function (SCDF). See "SamplingPatternRandomSamplingUnits" for more details.
statistic = "SpatialMean", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame"
estimates the spatial mean. See "SpatialMean" for more details.
statistic = "SpatialVariance", stratification = "CompactStratification", samplingPattern = "SamplingPatternRandomSamplingUnits", data = "data.frame"
estimates the spatial variance. See "SpatialVariance" for more details.

Examples

## 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)

[Package spcosa version 0.1-5 Index]