mseZ: Compute z-score of mean squared error

In cmerow/meteR: Fitting and Plotting Tools for the Maximum Entropy Theory of Ecology (METE)

Description

mseZ.meteDist Compute z-score of mean squared error

Usage

mseZ(x, ...)

## S3 method for class 'meteDist'
mseZ(x, nrep, return.sim = TRUE, type = c("rank",
  "cumulative"), relative = TRUE, log = FALSE, ...)

Arguments

x	a meteDist object
...	arguments to be passed to methods
nrep	number of simulations from the fitted METE distribution
return.sim	logical; return the simulated liklihood values
type	either "rank" or "cumulative"
relative	logical; if true use relative MSE
log	logical; if TRUE calculate MSE on logged distirbution. If FALSE use arithmetic scale

Details

mseZ.meteDist simulates from a fitted METE distribution (e.g. a species abundance distribution or individual power distribution) and calculates the MSE between the simulated data sets and the METE prediction. The distribution of these values is compared against the MSE of the data to obtain a z-score in the same was as logLikZ; see that help document for more details.

Value

list with elements

z

The z-score

sim

nrep Simulated values

Author(s)

Andy Rominger <[e-mail]>, Cory Merow

References

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

See Also

logLikZ

Examples

esf1=meteESF(spp=arth$spp,
              abund=arth$count,
              power=arth$mass^(4/3),
              minE=min(arth$mass^(4/3)))
sad1=sad(esf1)
mseZ(sad1, nrep=100, type='rank',return.sim=TRUE)

cmerow/meteR documentation built on May 13, 2019, 8:23 p.m.