| mask.check {secr} | R Documentation |
mask.check evaluates the effect of varying buffer width and
mask spacing on either the likelihood or density estimates from
secr.fit().
mask.check(object, buffers = NULL, spacings = NULL, poly = NULL,
LLonly = TRUE, realpar = NULL, session = 1, file = NULL,
drop = "", tracelevel = 0, ...)
object |
object of class ‘capthist’ or ‘secr’ |
buffers |
vector of buffer widths |
spacings |
vector of mask spacings |
poly |
matrix of two columns, the x- and y-coordinates of a bounding polygon (optional) |
LLonly |
logical; if TRUE then only the log likelihood is computed |
realpar |
list of parameter values |
session |
vector of session indices (used if |
file |
name of output file (optional) |
drop |
character vector: names of fitted secr object to omit |
tracelevel |
integer for level of detail in reporting (0,1,2) |
... |
other arguments passed to secr.fit |
Masks of varying buffer width and spacing are constructed with the
‘trapbuffer’ method in make.mask, using the detector locations
(‘traps’) from either a capthist object or a previous execution of
secr.fit. Default values are provided for buffers and
spacings if object is of class ‘secr’ (respectively c(1,
1.5, 2) and c(1, 0.75, 0.5) times the values in the existing
mask). The default for buffers will not work if a detector is
on the mask boundary, as the inferred buffer is then 0.
Variation in the mask may be assessed for its effect on –
the log-likelihood evaluated for given values of the
parameters (LLonly = TRUE)
estimates from maximizing the likelihood with each mask
(LLonly = FALSE)
realpar should be a list with one named component for each real
parameter (see Examples). It is relevant only if LLonly =
TRUE. realpar may be omitted if object is of class
‘secr’; parameter values are then extracted from object.
session should be an integer or character vector suitable for
indexing sessions in object, or in object$capthist if
object is a fitted model. Each session is considered
separately; a model formula that refers to session or uses session
covariates will cause an error.
If file is specified then detailed
results (including each model fit when LLonly = FALSE) are
saved to an external .RData file. Loading this file creates or
overwrites object(s) in the workspace: mask.check.output if
LLonly = TRUE, otherwise mask.check.output and
mask.check.fit. For multiple sessions these are replaced by
lists with one component per session (mask.check.outputs and
mask.check.fits). The drop argument is passed to
trim and applied to each fitted model; use it to save
space, at the risk of limiting further computation on the fitted
models.
tracelevel>0 causes more verbose reporting of progress during
execution.
The ... argument may be used to override existing settings in
object - for example, a conditional likelihood fit (CL =
T) may be selected even if the original model was fitted by
maximizing the full likelihood.
Array of log-likelihoods (LLonly = TRUE) or estimates
(LLonly = FALSE) for each combination of buffers and
spacings. The array has 3 dimensions if LLonly = FALSE
and both buffers and spacings have multiple levels;
otherwise it collapses to a matrix. Rows generally represent
buffers, but rows represent spacings if a single buffer
is specified.
mask.check() may fail if object is a fitted ‘secr’ model
and a data object named in the original call of secr.fit()
(i.e. object$call) is no longer in the working environment
(secr.fit arguments capthist, mask, verify & trace are
exempt). Fix by any of (1) applying mask.check directly to the
‘capthist’ object, specifying other arguments (buffers,
spacings, realpar) as needed, (2) re-fitting the model
and running mask.check in the same environment, (3) specifying
the offending argument(s) in ..., or (4) re-creating the required
data objects(s) in the working environment, possibly from saved inputs
in object (e.g., mytimecov <- myfit$timecov).
When LLonly = TRUE the functionality of mask.check
resembles that of the ‘Tools | ML SECR log likelihood’ menu option in
Density 5. The help page in Density 5 for ML SECR 2-D integration (see
index) may be helpful.
Warning messages from secr.fit are suppressed. ‘capthist’
data provided via the object argument are checked with
verify.capthist if tracelevel > 0.
The likelihood-only method is fast, but not definitive. It is
reasonable to aim for stability in the third decimal place of the log
likelihood. Slight additional variation in the likelihood may cause
little change in the estimates; the only way to be sure is to check
these by setting LLonly = FALSE.
The performance of a mask depends on the detection function; be sure
to set the detectfn argument appropriately. The hazard rate
function has a fat tail that can be problematic.
When provided with an ‘secr’ object, mask.check
constructs a default vector of buffer widths as multiples of the
buffer used in object even though that value is not saved
explicitly. For this trick, detector locations in
traps(object$capthist) are compared to the bounding box of
object$mask; the base level of buffer width is the maximum
possible within the bounding box.
Borchers, D. L. and Efford, M. G. (2008) Spatially explicit maximum likelihood methods for capture–recapture studies. Biometrics 64, 377–385.
Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture–recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density/.
## Not run:
## from a capthist object, specifying almost everything
mask.check (possumCH, spacings = c(20, 30), buffers =c(200, 300),
realpar = list(g0 = 0.2, sigma = 50), CL = TRUE)
## from a fitted model, using defaults
mask.check (stoat.model.HN)
## LL did not change with varying buffer (rows) or spacing (cols):
## 78.125 58.59375 39.0625
## 1000 -144.0015 -144.0015 -144.0015
## 1500 -144.0017 -144.0017 -144.0017
## 2000 -144.0017 -144.0017 -144.0017
## fit new models for each combination of buffer & spacing,
## and save fitted models to a file
mask.check (stoat.model.HN, buffers = 1500, spacings =
c(40,60,80), LLonly = FALSE, file = "test", CL = TRUE)
## look in more detail at the preceding fits
## restores objects `mask.check.output' and `mask.check.fit'
load("test.RData")
lapply(mask.check.fit, predict)
lapply(mask.check.fit, derived)
## multi-session data
mask.check(ovenbird.model.1, session = c("2005","2009"))
## clipping mask
txtfilename <- system.file("extdata/possumarea.txt", package = "secr")
possumarea <- read.table(txtfilename, header = TRUE)
mask.check (possum.model.0, spacings = c(20, 30), buffers =
c(200, 300), poly = possumarea, LLonly = FALSE,
file = "temp", CL = TRUE)
## review fitted models
load ("temp.RData")
par(mfrow = c(2,2), mar = c(1,4,4,4))
for (i in 1:4) {
plot(traps(mask.check.fit[[i]]$capthist), border = 300,
gridlines = FALSE)
plot(mask.check.fit[[i]]$mask, add = TRUE)
lines(possumarea)
text ( 2698618, 6078427, names(mask.check.fit)[i])
box()
}
par(mfrow = c(1,1), mar = c(5,4,4,2) + 0.1) ## defaults
## End(Not run)