calRaster: Rescale isoscape using linear regression
In assignR: Infer Geographic Origin from Isotopic Data

calRaster

R Documentation

Rescale isoscape using linear regression

Description

This function uses known-origin tissue data to rescale a map of environmental isotope values to a map of tissue value (and associated uncertainty) using a linear regression model.

Usage

calRaster(known, isoscape, mask = NULL, interpMethod = 2, NA.value = NA, 
  ignore.NA = TRUE, genplot = TRUE, outDir =  NULL, verboseLM = TRUE)

Arguments

`known`	subOrigData or SpatVector. Known-origin tissue isotope data from the subOrigData function or provided by user. User-provided data must be formatted as a subOrigData object (see `subOrigData`) or a SpatVector with point geometry in which the first data field contains the measured tissue isotope value and the second the 1 standard deviation uncertainty on that value.
`isoscape`	SpatRaster. Isoscape raster with two layers. The first one is the mean and the second is one standard deviation.
`mask`	SpatVector. Polygon layer that constrains the area of the output rasters. If this is not provided, the entire area of `isoscape` is returned.
`interpMethod`	numeric. 1 or 2. Designate one of two methods for extracting values from `isoscape`. If 1, values for the cell in which a point falls are returned. If 2 the returned values are interpolated (bilinear interpolation) from the values of the four adjacent grid cells.
`NA.value`	NA or numeric. Value representing the absence of data in `isoscape`. Commonly used values include NA, -9999, and 9999.
`ignore.NA`	logical. If NA values are extracted from `isoscape` at the location of samples in known, ignore these values and proceed with `calRaster`.
`genplot`	logical. Plot the results.
`outDir`	character string. Directory path to which output will be saved. If NULL no files are written.
`verboseLM`	logical. Print out the linear regression results.

Value

Returns an object of class “rescale”.

`isoscape.rescale`	SpatRaster. `isoscape` rescaled using a linear regression model between values of `known` and `isoscape`. The first layer is the mean prediction and the second the standard deviation.
`lm.data`	data.frame. Known origin data and extracted `isoscape` values used for linear regression modeling.
`lm.model`	list. Linear regression model.

Examples

# load hydrogen isotope data for human hair in North America
d = subOrigData(group = "Modern human", mask = naMap, niter = 100, genplot = FALSE)

# rescale from environmental isoscape to tissue isoscape
r = calRaster(d, d2h_lrNA, naMap)

assignR documentation built on Sept. 1, 2023, 5:05 p.m.