IsoriX (version 0.6)

isofind: Infer spatial origins

Description

This function performs the assignment of organims of unknown origins.

Usage

isofind(assign.data, isoscape, calibfit, mask = NA, verbose = interactive())

Arguments

assign.data

A dataframe containing the assignment data (see note below)

isoscape

The output of the function isoscape

calibfit

The output of the function calibfit

mask

A SpatialPolygons of a mask to replace values on all rasters by NA inside polygons (see details)

verbose

A logical indicating whether information about the progress of the procedure should be displayed or not while the function is running. By default verbose is TRUE if users use an interactive R session and FALSE otherwise.

Value

This function returns a list of class isorix containing itself three lists (indiv, group, and sp.points) storing all rasters built during assignment and the spatial points for sources and calibration. The list indiv contains three stack of raster layers: one storing the value of the test statistic ("stat"), one storing the value of the variance of the test statistic ("var") and one storing the p-value of the test ("pv"). The list group contains one raster storing the p-values of the assignment for the group. The list sp.points contains two spatial point objects: source.points and calib.points.

Details

An assignment is a comparison, for a given organism, of the predicted isotopic value at its location of origin and the predicted isotopic value at each location of the isoscape. The difference between these two values constitute the statistic of the assingment test. Under the null hypothesis (when the organism is at a location with the same isotopic value than its original location), the test statistics follows a normal distribution with mean zero and a certain variance that stems from both the isoscape model fits and the calibration fit. The function isofind computes the map of p-value for such an assignment test (i.e. the p-values in all locations of the isoscape) for all individuals in the dataframe assign.data. The function also performs a single assignment for the entire group by combining the individual p-value maps using the Fisher's method (Fisher 1925).

A mask can be used so to remove all values falling in the mask. This can be useful for performing for example assignments on lands only and discard anything falling in large bodies of water (see example). By default our OceanMask is considered. Setting mask to NULL allows to prevent this automatic behaviour.

References

Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd (Edinburgh). ISBN 0-05-002170-2.

See Also

IsoriX for the complete workflow

Examples

# NOT RUN {
## The examples below will only be run if sufficient time is allowed
## You can change that by typing e.g. IsoriX.options(example_maxtime = XX)
## if you want to allow for examples taking up to ca. XX seconds to run
## (so don't write XX but put a number instead!)

if(IsoriX.getOption("example_maxtime") > 120) {

## We fit the models for Germany
GNIPDataDEagg <- queryGNIP(data = GNIPDataDE)

GermanFit <- isofit(iso.data = GNIPDataDEagg)


## We build the isoscape
isoscape <- isoscape(elevation.raster = ElevRasterDE,
                     isofit = GermanFit)


## We fit the calibration model
calib <- calibfit(calib.data = CalibDataAlien,
                  isofit = GermanFit)

## We perform the assignment on land only
assignment.dry <- isofind(assign.data = AssignDataAlien,
                          isoscape = isoscape,
                          calibfit = calib)

## perform the assignment on land and water
assignment <- isofind(assign.data = AssignDataAlien,
                      isoscape = isoscape,
                      calibfit = calib,
                      mask = NULL)

## We plot the group assignment
plot(assignment, who = "group", mask = list(mask = NULL))

plot(assignment.dry, who = "group", mask = list(mask = NULL))

## We plot the assignment for the 8 first individuals
plot(assignment.dry, who = 1:8,
     sources = list(draw = FALSE),
     calib = list(draw = FALSE))

## We plot the assignment for the individual "Alien_10"
plot(assignment.dry, who = "Alien_10")

}

# }