troubleshooting: Problems in Fitting SECR Models
Description
Although secr.fit is quite robust, it does not always
work. Inadequate data or an overambitious model occasionally cause
numerical problems in the algorithms used for fitting the model, or
problems of identifiability, as described for capture--recapture models
in general by Gimenez et al. (2004). Here are some tips that may help
you.secr.fit finishes, but some or all of the variances are missing
This usually means the model did not fit and the estimates should not
be used. Extremely large variances or standard errors also indicate
problems.
- Try another maximization method (
method =
'Nelder-Mead'is more robust than the default). A message may suggest
usingmethod = 'BFGS', but this is not so often useful as
originally thought. The same maximum likelihood should be found
regardless of method, so AIC values are comparable across methods. - Repeat the maximization with different starting values. You can use
secr.fit(..., start = last.model)wherelast.modelis a
previously fitted secr object. - Try a finer mask (e.g., vary argument
nxinmake.mask). Check that the extent of the mask matches your
data. - The maximization algorithms work poorly when the beta coefficients
are of wildly different magnitude. This may happen when using
covariates: ensure beta coefficients are similar (within a factor of
5--10 seems adequate, but this is not based on hard evidence) by scaling
any covariates you provide. This can be achieved by setting the
typsizeargument ofnlmor theparscalecontrol argument ofoptim. - Examine the model. Boundary values (e.g., g0 near 1.0) may give
problems. In the case of more complicated models you may gain insight by
fixing the value of a difficult-to-estimate parameter (argument
fixed).
See also the section `Potential problems' in
../doc/secr-densitysurfaces.pdf.secr.fit finishes with warning nlm code 1 or 3
These conditions do not invariably indicate a failure of model
fitting. Proceed with caution, checking as suggested in the preceding
section.secr.fit crashes part of the way through maximization
A feature of the maximization algorithm used by default in nlm
is that it takes a large step in the parameter space early on in the
maximization. The step may be so large that it causes floating point
underflow or overflow in one or more real parameters. This can be
controlled by passing the `stepmax' argument of nlm in the
...argument of secr.fit (see first example). See also the
previous point about scaling of covariates.secr.fit crashes near end
When fitting a model with verify = TRUE you see the log
likelihood converge (assuming trace = TRUE), but
secr.fit crashes without returning, perhaps with an obscure
message referring to nls, or "Error in integrate... : the
integral is probably divergent". This is most likely due to numerical
problems in the optional bias check with bias.D. Simply
set verify = FALSE and repeat.secr.fit demands more memory than is available
This is a problem particularly when using individual covariates in a
model fitted by maximizing the conditional likelihood. The memory
required is then roughly proportional to the product of the number of
individuals, the number of occasions, the number of detectors and the
number of latent classes (for finite-mixture models). When maximizing
the full-likelihood, substitute `number of groups' for `number of
individuals'. [The limit is reached in external C used for the
likelihood calculation, which uses the R function `R_alloc'.]
The mash function may be used to reduce the number of
detectors when the design uses many identical and independent
clusters. Otherwise, apply your ingenuity to simplify your model,
e.g., by casting `groups' as `sessions'. Memory is less often an issue
on 64-bit systems (see link below).References
Gimenez, O., Viallefont, A., Catchpole, E. A., Choquet, R. and Morgan, B. J. T. (2004)
Methods for investigating parameter redundancy. Animal Biodiversity and
Conservation 27, 561--572.