How do I install secr?
Follow the usual procedure for installing binaries from CRAN, or...
Under Windows, it is simplest to install the package binary from the
Rgui. Save the file secr_1.4.x.zip to a local folder ('x' is the release
number) and use the menu option "Packages | Install packages(s) from
local zip files...".
For other systems you may need to install the source package
secr_1.4.x.tar.gz.
Whatever your system, you also need to get the package abind (use
Packages | Install package(s)... in Windows to download from
CRAN). Other required packages (MASS, nlme, stats)
should be available as part of your Rinstallation.How can I get help?
There are three general ways of displaying documentation from within R.
Firstly, you can bring up help pages for particular functions from the
command prompt. For example:
? secr.fit
Secondly, help.search() lets you ask for a list of the help pages on a
vague topic. From Rversion 2.8.0 you can use just ??. For example:
?? 'linear models'
Thirdly, you can display various documents:
RShowDoc ('secr-manual', package='secr')
RShowDoc ('secr-overview', package='secr')
See below for more Rtips.How should I report a problem?
If you get really stuck or find something you think is a bug then please
report the problem. There is a support forum at
www.phidot.org/forum under 'DENSITY/secr'. Please read the FAQ
there before posting.
You may be asked to send an actual dataset - ideally, the simplest one
that exhibits the problem. The correct address for this is
density.software@otago.ac.nz. Use save to wrap
several Robjects together in one .RData file, e.g.,
save('captdata', 'secrdemo.0', 'secrdemo.b', file =
'mydata.RData'). Also, paste into the text of your message the output
from packageDescription("secr").
Needless to say, we cannot promise to solve all problems.Why do I get different answers from secr and Density?
Strictly speaking, this should not happen if you have specified the same
model and likelihood, although you may see a little variation due to the
different maximization algorithms. Likelihoods (and estimates) may
differ if you use different integration meshes (habitat masks), which
can easily happen because the programs differ in how they set up the
mesh. If you want to make a precise comparison, save the Density mesh
to a file and read it into secr, or vice versa.
Extreme data, especially rare long-distance movements, may be handled
differently by the two programs. The 'minprob' component of the
'details' argument of secr.fit sets a threshold of probability
for capture histories (smaller values are all set to minprob), whereas
Density has no explicit limit. In the current version the default
minprob has been reduced from 1e-20 to 1e-50. If you find a discrepancy
with Density it may be worth lowering minprob even further.How can I speed up model fitting and model selection?
If you don't need to model variation in density over space or time then
consider maximizing the conditional likelihood in secr.fit (CL =
TRUE). This reduces the complexity of the optimization problem,
especially where there are several sessions and you want
session-specific density estimates (by default, derived returns a
separate estimate for each session even if the detection parameters are
constant across sessions).
Check the extent and spacing of the habitat mask that you are using.
Execution time is roughly proportional to the number of mask points.
Default settings can lead to very large masks for detector arrays that
are elongated 'north-south' because the number of points in the
east-west direction is fixed. Compare results with a much sparser mask
(e.g., nx = 32 instead of nx = 64).
Do you really need to fit that complex model? Chasing down small
decrements in AIC is so last-century. Remember that detection parameters
are mostly nuisance parameters, and models with big differences in AIC
may barely differ in their density estimates. This is a good topic for
further research - we seem to need a 'focussed information criterion'
(Claeskens and Hjort 2008) to discern the differences that matter.
Use score.test to compare nested models. At each stage this
requires only the more simple model to have been fitted in full; further
processing is required to obtain a numerical estimate of the gradient of
the likelihood surface for the more complex model, but this is much
faster than maximizing the likelihood.Things You Might Need To Know About R
The function findFn in package sos lets you search CRAN for
R functions by matching text in their documentation.
There is now a vast amount of Radvice available on the web. For the
terminally frustrated, 'R inferno' by Patrick Burns is recommended
(www.burns-stat.com/pages/Tutor/R_inferno.pdf). "If you are using
R and you think you're in hell, this is a map for you".
Method functions for S3 classes cannot be listed in the usual way by
typing the function name at the Rprompt because they are 'hidden' in a
namespace. Get around this with getAnywhere(). For example:
getAnywhere(print.secr)
R objects have 'attributes' that usually are kept out of sight.
Important attributes are 'class' (all objects), 'dim' (matrices and
arrays) and 'names' (lists). secr hides quite a lot of useful data
as named 'attributes'. Usually you will use summary and extraction
methods (traps, covariates, usage etc.) to view and change
the attributes of the various classes of object in secr. If you're
curious, you can reveal the lot with 'attributes'. For example:
data(secrdemo)
traps(captdata) ## extraction method for 'traps'
attributes(captdata) ## all attributes