Simulate From Fitted secr Model
Simulate a spatially distributed population, sample from that population with an array of detectors, and optionally fit an SECR model to the simulated data.
## S3 method for class 'secr'
simulate(object, nsim = 1, seed = NULL, maxperpoly = 100,
chat = 1, ...)
sim.secr(object, nsim = 1, extractfn = function(x) c(deviance =
deviance(x), df = df.residual(x)), seed = NULL, maxperpoly = 100,
data = NULL, tracelevel = 1, hessian = c("none", "auto", "fdHess"),
start = object$fit$par, ncores = NULL)
sim.detect(object, popnlist, maxperpoly = 100, renumber = TRUE)object |
a fitted secr model |
nsim |
integer number of replicates |
seed |
either NULL or an integer that will be used in a call to |
maxperpoly |
integer maximum number of detections of an individual in one polygon or transect on any occasion |
chat |
real value for overdispersion parameter |
ncores |
integer number of threads used by |
extractfn |
function to extract output values from fitted model |
data |
optional list of simulated data saved from previous call to |
tracelevel |
integer for level of detail in reporting (0,1,2) |
hessian |
character or logical controlling the computation of the Hessian matrix |
start |
vector of starting ‘beta’ values for |
... |
other arguments (not used) |
popnlist |
list of popn objects |
renumber |
logical; if TRUE then |
For each replicate, simulate.secr calls sim.popn to
generate session- and group-specific realizations of the (possibly
inhomogeneous) 2-D Poisson distribution fitted in object, across
the habitat mask(s) in object. Group subpopulations are combined
using rbind.popn within each session; information to
reconstruct groups is retained in the individual-level factor
covariate(s) of the resulting popn object (corresponding to
object$groups). Each population is then sampled using the fitted
detection model and detector (trap) array(s) in object.
The random number seed is managed as in simulate.lm.
Certain model types are not supported by simulate.secr. These
include models fitted using conditional likelihood (object$CL =
TRUE), telemetry models and exotic behavioural response models.
Detector type is determined by detector(traps(object$capthist)).
sim.secr is a wrapper function. If data = NULL (the
default) then it calls simulate.secr to generate nsim new datasets. If
data is provided then nsim is taken to be
length(data). secr.fit is called to fit the original model
to each new dataset. Results are summarized according to the
user-provided function extractfn. The default extractfn
returns the deviance and its degrees of freedom; a NULL value for
extractfn returns the fitted secr objects after
trimming to reduce bulk. Simulation uses the detector type
of the data, even when another likelihood is fitted (this is the case
with single-catch data, for which a multi-catch likelihood is fitted).
Warning messages from secr.fit are suppressed.
extractfn should be a function that takes an secr object
as its only argument.
tracelevel=0 suppresses most messages; tracelevel=1 gives a
terse message at the start of each fit; tracelevel=2 also sets
‘details$trace = TRUE’ for secr.fit, causing each likelihood
evaluation to be reported.
hessian controls computation of the Hessian matrix from which
variances and covariances are obtained. hessian replaces the
value in object\$details. Options are "none" (no variances),
"auto" (the default) or "fdhess" (see secr.fit). It is OK
(and faster) to use hessian="none" unless extractfn needs
variances or covariances. Logical TRUE and FALSE are interpreted by
secr.fit as "auto" and "none".
If ncores = NULL then the existing value from the environment variable
RCPP_PARALLEL_NUM_THREADS is used (see setNumThreads).
sim.capthist is a more direct way to simulate data from a null
model (i.e. one with constant parameters for density and detection), or
from a time-varying model.
sim.detect is a function used internally that will not usually be
called directly.
For simulate.secr, a list of data sets (‘capthist’ objects). This
list has class c("secrdata", "list"); the initial state of the random
number generator (roughly, the value of .Random.seed) is stored as the
attribute ‘seed’.
The value from sim.secr depends on extractfn: if that
returns a numeric vector of length n.extract then the value is a
matrix with dim = c(nsim, n.extract) (i.e., the matrix has one
row per replicate and one column for each extracted value). Otherwise,
the value returned by sim.secr is a list with one component per
replicate (strictly, an object of class = c("secrlist", "list")). Each
simulated fit may be retrieved in toto by specifying
extractfn = identity, or slimmed down by specifying
extractfn = NULL or extractfn = trim, which are
equivalent.
For either form of output from sim.secr the initial state of the
random number generator is stored as the attribute ‘seed’.
For sim.detect a list of ‘capthist’ objects.
The value returned by simulate.secr is a list of ‘capthist’
objects; if there is more than one session, each ‘capthist’ is itself a
sort of list .
The classes ‘secrdata’ and ‘secrlist’ are used only to override the ugly
and usually unwanted printing of the seed attribute. However, a few
other methods are available for ‘secrlist’ objects
(e.g. plot.secrlist).
The default value for start in sim.secr is the previously
fitted parameter vector. Alternatives are NULL or object$start.
## previously fitted model
simulate(secrdemo.0, nsim = 2)
## Not run:
## The following has been superceded by secr.test()
## this would take a long time...
sims <- sim.secr(secrdemo.0, nsim = 99)
deviance(secrdemo.0)
devs <- c(deviance(secrdemo.0),sims$deviance)
quantile(devs, probs=c(0.95))
rank(devs)[1] / length(devs)
## to assess bias and CI coverage
extrfn <- function (object) unlist(predict(object)["D",-1])
sims <- sim.secr(secrdemo.0, nsim = 50, hessian = "auto",
extractfn = extrfn)
sims
## with a larger sample, could get parametric bootstrap CI
quantile(sims[,1], c(0.025, 0.975))
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.