dg.envelope(X, ..., nsim = 19, nsimsub=nsim-1, nrank = 1, alternative=c("two.sided", "less", "greater"), leaveout=1, interpolate = FALSE, savefuns=FALSE, savepatterns=FALSE, verbose = TRUE)
"ppp"
,
"lpp"
or "pp3"
) or a fitted point process model
(object of class "ppm"
, "kppm"
or "slrm"
).
nsim
repetitions of the basic test, each involving nsimsub
simulated
realisations, so there will be a total
of nsim * (nsimsub + 1)
simulations.
nsim
simulated
values. A rank of 1 means that the minimum and maximum
simulated values will be used.
alternative="two.sided"
, the default)
or a one-sided test with a lower critical boundary
(alternative="less"
) or a one-sided test
with an upper critical boundary (alternative="greater"
).
"fv"
.
X
is a point pattern, the null hypothesis is CSR. If X
is a fitted model, the null hypothesis is that model.
Baddeley, A., Hardegen, A., Lawrence, L., Milne, R.K., Nair, G.M. and Rakshit, S. (2015) Pushing the envelope: extensions of graphical Monte Carlo tests. Submitted for publication.
dg.test
,
mad.test
,
envelope
ns <- if(interactive()) 19 else 4
E <- dg.envelope(swedishpines, Lest, nsim=ns)
E
plot(E)
Eo <- dg.envelope(swedishpines, Lest, alternative="less", nsim=ns)
Ei <- dg.envelope(swedishpines, Lest, interpolate=TRUE, nsim=ns)
Run the code above in your browser using DataLab