mseZ {meteR} | R Documentation |
mseZ.meteDist
Compute z-score of mean squared error
mseZ(x, ...)
## S3 method for class 'meteDist'
mseZ(x, nrep, return.sim = TRUE, type = c("rank",
"cumulative"), relative = TRUE, log = FALSE, ...)
x |
a |
... |
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 |
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.
list with elements
The z-score
nrep
Simulated values
Andy Rominger <ajrominger@gmail.com>, Cory Merow
Harte, J. 2011. Maximum entropy and ecology: a theory of abundance, distribution, and energetics. Oxford University Press.
logLikZ
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)