details | R Documentation |

The function `secr.fit`

allows many options. Some of these are used
infrequently and have been bundled as a single argument `details`

to simplify the documentation. They are described here.

`details$autoini`

specifies the session number from which to compute starting
values (multi-session data only; default 1). From 4.0.0, the character value ‘all’
first forms a single-session capthist using `join`

(); this may be slow or not
work at all (especially with telemetry data).

`details$centred`

= TRUE causes coordinates of both traps and mask
to be centred on the centroid of the traps, computed separately for each
session in the case of multi-session data. This may be necessary to
overcome numerical problems when x- or y-coordinates are large
numbers. The default is not to centre coordinates.

`details$chat`

optionally specifies the overdispersion
of unmarked sightings Tu and unidentified marked sightings Tm. It is used only
for mark-resight models, and is usually computed within `secr.fit`

(`details$nsim > 0`

), but may be provided by the user. For a single session 'chat' is a vector of length 2; for multiple sessions it is a 2-column matrix.

`details$chatonly`

= TRUE used with `details$nsim > 0`

causes the
overdispersion statistics for sighting counts Tu and Tm to be estimated and
returned as a vector or 2-column matrix (multi-session models), with no further
model fitting.

`details$contrasts`

may be used to specify the coding of factor predictors. The value should be suitable for the 'contrasts.arg' argument of `model.matrix`

. See ‘Trend across sessions’ in secr-multisession.pdf for an example.

`details$convexpolygon`

may be set to FALSE for searches of non-convex polygons. This is slower than the default which requires poygons to be convex east-west (secr-polygondetectors.pdf).

`details$debug`

is an integer code used to control the printing of intermediate
values (1,2) and to switch on the R code browser (3). In ordinary use it should not be
changed from the default (0).

`details$Dfn`

is a function for reparameterizing density models; this is set internally when Dlambda = TRUE. Exotic variations may be specified directly by the user when Dlambda = FALSE. The defaults (Dfn = NULL, Dlambda = FALSE) leave the original density model unchanged. Note there is no connection to userDfn (except that the two are incompatible).

`Dlambda`

if TRUE causes reparameterization of density as the session-on-session finite rate of increase `lambda`

. Details at (secr-trend.pdf).

`details$distribution`

specifies the distribution of the number of
individuals detected `n`

; this may be conditional on the number in the
masked area ("binomial") or unconditional ("poisson").
`distribution`

affects the sampling variance of the estimated
density. The default is "poisson". The component ‘distribution’ may also
take a numeric value larger than nrow(capthist), rather than "binomial"
or "poisson". The likelihood then treats n as a binomial draw from a
superpopulation of this size, with consequences for the variance of
density estimates. This can help to reconcile MLE with Bayesian
estimates using data augmentation.

`details$fastproximity`

controls special handling of data from binary proximity and count detectors. If TRUE and other conditions are met (no temporal variation or groups) then a multi-occasion capthist is automatically reduced to a count for a single occasion and further compressed by storing only non-zero counts, which can greatly speed up computation of the likelihood (default TRUE).

`details$fixedbeta`

may be used to fix values of beta
parameters. It should be a numeric vector of length equal to the total
number of beta parameters (coefficients) in the model. Parameters to be
estimated are indicated by NA. Other elements should be valid values on
the link scale and will be substituted during likelihood
maximisation. Check the order of beta parameters in a previously fitted
model.

`details$grain`

sets the grain argument for multithreading in RcppParallel parallelFor (default 1).
`details$grain = 0`

suppresses multithreading (equivalent to `ncores = 1`

).

`details$hessian`

is a character string controlling the computation
of the Hessian matrix from which variances and covariances are obtained.
Options are "none" (no variances), "auto" (the default) or "fdhess" (use
the function fdHess in nlme). If "auto" then the Hessian from the
optimisation function is used. See also method = "none" below.

`details$ignoreusage`

= TRUE causes the function to ignore
usage (varying effort) information in the traps component. The default
(`details$ignoreusage`

= FALSE) is to include usage in the model.

`details$intwidth2`

controls the half-width of the interval
searched by optimise() for the maximum likelihood when there is a single
parameter. Default 0.8 sets the search interval to `(0.2s, 1.8s)`

where `s`

is the ‘start’ value.

`details$knownmarks`

= FALSE causes secr.fit to fit a zero-truncated
sightings-only model that implicitly estimates the number of marked individuals,
rather than inferring it from the number of rows in the capthist object.

`details$LLonly`

= TRUE causes the function to returns a single
evaluation of the log likelihood at the ‘start’ values.

`details$maxdistance`

sets a limit to the centroid-to-mask distances considered. The centroid is the geometric mean of detection locations for each individual. If no limit is specified then summation is over all mask points. Specifying `maxdistance`

can speed up computation; it is up to the user to select a limit that is large enough not to affect the likelihood (`5\sigma`

?).

`details$miscparm`

(default NULL) is an optional numeric vector of
starting values for additional parameters used in a user-supplied
distance function (see ‘userdist’ below). If the vector has a names
attribute then the names will be used for the corresponding coefficients
(‘beta’ parameters) which will otherwise be named ‘miscparm1’,
miscparm2' etc. These parameters are constant across each model and do
not appear in the model formula, but are estimated along with other
coefficients when the likelihood is maximised. Any transformation (link
function) etc. is handled by the user in the userdist function. The
coefficients appear in the output from `coef.secr`

and
`vcov.secr`

, but not `predict.secr`

.

`details$newdetector`

specifies a detector type to use for this fit,
replacing the previous `detector(traps(capthist))`

. The value may be
a vector (one value per occasion) or for multi-session data, a list of vectors.
A scalar value (e.g. "proximity") is otherwise used for all occasions and sessions.
The true detector type is usually known and will be specified in the 'traps' attribute;
`newdetector`

is useful in simulation studies that examine the effect of misspecification. The capthist component of the output from secr.fit has the new type.

`details$nsim`

specifies the number of replicate simulations to
perform to estimate the overdispersion statistics for the sighting counts
Tu and Tm. See also `details$chat`

and `details$chatonly`

.

`details$param`

chooses between various parameterisations of the
SECR model. The default `details$param = 0`

is the formulation in
Borchers and Efford (2008) and later papers.

`details$param = 1`

was once used to select the Gardner & Royle parameterisation of
the detection model (*p0*, `\sigma`

; Gardner et al. 2009) when
the detector type is ‘multi’. This parameterisation was discontinued in 2.10.0.

`details$param = 2`

selects parameterisation in terms of
(`esa(g_0, \sigma)`

, `\sigma`

) (Efford and Mowat 2014).

`details$param = 3`

selects parameterisation in terms of
(`a_0(\lambda_0, \sigma)`

, `\sigma`

) (Efford and Mowat 2014). This
parameterization is used automatically if a0 appears in the model (e.g.,
a0 ~ 1).

`details$param = 4`

selects parameterisation of sigma in terms of
the coefficient sigmak and constant c (sigma = sigmak /
D^0.5 + c) (Efford et al. 2016). If c is not included explicitly in
the model (e.g., c ~ 1) then it is set to zero. This
parameterization is used automatically if sigmak appears in the model (e.g.,
sigmak ~ 1)

`details$param = 5`

combines parameterisations (3) and (4) (first
compute sigma from D, then compute lambda0 from sigma).

`details$savecall`

determines whether the full call to `secr.fit`

is
saved in the output object. The default is TRUE except when called by
`par.secr.fit`

as names in the call are then evaluated, causing the
output to become unwieldy.

`details$splitmarked`

determines whether the home range centre of marked
animals is allowed to move between the marking and sighting phases of a spatial
capture–mark–resight study. The default is to assume a common home-range centre
(`splitmarked = FALSE`

).

`details$telemetrytype`

determines how telemetry data in the
attribute ‘xylist’ are treated. ‘none’ causes the xylist data to be
ignored. ‘dependent’ uses information on the sampling distribution of
each home-range centre in the SECR likelihood. ‘concurrent’ does that
and more: it splits capthist according to telemetry status and appends
all-zero histories to the telemetry part for any animals present in
xylist. The default is ‘concurrent’.

`details$usecov`

selects the mask covariate to be used for
normalization. NULL limits denominator for normalization to
distinguishing habitat from non-habitat.

`details$userDfn`

is a user-provided function for modelling a density
surface. See secr-densitysurfaces.pdf

`details$userdist`

is either a function to compute non-Euclidean
distances between detectors and mask points, or a pre-computed matrix of
such distances. The first two arguments of the function should be
2-column matrices of x-y coordinates (respectively `k`

detectors and
`m`

mask points). The third argument is a habitat mask that defines
a non-Euclidean habitat geometry (a linear geometry is described in
documentation for the package ‘secrlinear’). The matrix
returned by the function must have exactly `k`

rows and `m`

columns. When called with no arguments the function should return a
character vector of names for the required covariates of ‘mask’,
possibly including the dynamically computed density 'D' and a parameter
‘noneuc’ that will be fitted. A slightly expanded account is at
userdist, and full documentation is in the separate
document secr-noneuclidean.pdf.

**Do not use ‘userdist’ for polygon or transect detectors**

Efford, M. G., Dawson, D. K., Jhala, Y. V. and Qureshi, Q. (2016)
Density-dependent home-range size revealed by spatially explicit
capture–recapture. *Ecography* **39**, 676–688.

Efford, M. G. and Mowat, G. (2014) Compensatory heterogeneity in
capture–recapture data.*Ecology* **95**, 1341–1348.

Gardner, B., Royle, J. A. and Wegan, M. T. (2009) Hierarchical models
for estimating density from DNA mark-recapture studies. *Ecology*
**90**, 1106–1115.

Royle, J. A., Chandler, R. B., Sun, C. C. and Fuller, A. K. (2013)
Integrating resource selection information with spatial
capture–recapture. *Methods in Ecology and Evolution* **4**, 520–530.

`secr.fit `

, userdist

```
## Not run:
## Demo of miscparm and userdist
## We fix the usual 'sigma' parameter and estimate the same
## quantity as miscparm[1]. Differences in CI reflect the implied use
## of the identity link for miscparm[1].
mydistfn3 <- function (xy1,xy2, mask) {
if (missing(xy1)) return(character(0))
xy1 <- as.matrix(xy1)
xy2 <- as.matrix(xy2)
miscparm <- attr(mask, 'miscparm')
distmat <- edist(xy1,xy2) / miscparm[1]
distmat
}
fit0 <- secr.fit (captdata)
fit <- secr.fit (captdata, fixed = list(sigma=1), details =
list(miscparm = c(sig = 20), userdist = mydistfn3))
predict(fit0)
coef(fit)
## End(Not run)
```

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