empiricalSAR: Empirical SAR or EAR
In meteR: Fitting and Plotting Tools for the Maximum Entropy Theory of Ecology (METE)

Description Usage Arguments Details Value Author(s) References See Also Examples

computes observed SAR or EAR from raw data

1	empiricalSAR(spp, abund, row, col, x, y, Amin, A0, EAR = FALSE)

`spp`	vector of species identities
`abund`	numberic vector abundances associated with each record
`row`	identity of row in a gridded landscape associated with each record, or desired number of rows to divide the landcape into
`col`	identity of column in a gridded landscape associated with each recod, or desired number of columns to divide the landcape into
`x`	the x-coordinate of an individual if recorded
`y`	the y-coordinate of an individual if recorded
`Amin`	the smallest area, either the anchor area for upscaling or the desired area to downscale to
`A0`	the largest area, either the area to upscale to or the total area from which to downscale
`EAR`	logical, should the EAR or SAR be computed

Currently only doublings of area are supported. There are several options for specifying areas. Either row and col or x and y must be provided for each data entry (i.e. the length of row and col or x and y must equal the length of spp and abund). If x and y are provided then the landscape is gridded either by specifying Amin (the size of the smallest grid cell) or by providing the number or desired rows and columns via the row and col arguments. If only row and col are provided these are taken to be the row and column identities of each data entry

an object of class sar inheriting from data.frame with columns A and S giving area and species richness, respectively

Andy Rominger <ajrominger@gmail.com>, Cory Merow

Harte, J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press.

meteESF, meteSAR, downscaleSAR, upscaleSAR

data(anbo)
anbo.obs.sar <- empiricalSAR(anbo$spp, anbo$count, anbo$row, anbo$col, Amin=1, A0=16)
plot(anbo.obs.sar)
anbo.obs.ear <- empiricalSAR(anbo$spp, anbo$count, anbo$row, anbo$col, Amin=1, A0=16, EAR=TRUE)
plot(anbo.obs.ear)

## empirical SAR from simulated x, y data
anbo$x <- runif(nrow(anbo), 0, 1) + anbo$column
anbo$y <- runif(nrow(anbo), 0, 1) + anbo$row
meteSAR(anbo$spp, anbo$count, x=anbo$x, y=anbo$y, row=4, col=4)