A place for hints and miscellaneous advice.
Follow the usual procedure for installing from CRAN archive (see menu item Packages | Install package(s)... in Windows). You also need to get the package abind from CRAN.
Like other contributed packages, secr needs to be loaded before
each use e.g.,library(secr).
You can learn about changes in the current version with
news(package = "secr").
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
or
?secr.fit
Secondly, help.search() lets you ask for a list of the help pages on a vague topic (or just use ?? at the prompt). For example:
?? "linear models"
Thirdly, you can display various secr documents listed in
secr-package.
Tip: to search all secr help pages open the pdf version of the manual in Acrobat Reader (secr-manual.pdf; see also ?secr) and use <ctrl> F.
There is a support forum at http://www.phidot.org/forum/ under
`DENSITY|secr' and another at secrgroup. See below for
more R tips. Some specific problems with secr.fit are covered in
Troubleshooting.
If you get really stuck or find something you think is a bug then please report the problem to one of the online lists.
You may be asked to send an actual dataset - ideally, the simplest one
that exhibits the problem. Use save to wrap
several R objects 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" ).
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 lower threshold of
probability for capture histories (smaller values are all set to
minprob), whereas Density has no explicit limit.
There are many ways - see Speed tips and secr-troubleshooting.pdf.
Keep the number of mask points to a minimum and avoid detection covariates with many levels.
Some computations can be run in parallel on multiple processors (most desktops these days have multiple cores). Likelihood calculations in secr.fit assign capture histories to multiple parallel threads whenever possible.
The number of threads (cores) is controlled by an environment variable set by setNumThreads or the 'ncores' argument of some functions.
Yes. See ?timevaryingcov. However, a more direct way to control for varying effort is provided - see the `usage' atribute, which now allows a continuous measure of effort (secr-varyingeffort.pdf).
A tip: covariate models for detection fit more quickly when the covariate takes only a few different values. Use binCovariate to bin values.
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 R advice available on the web. For the terminally frustrated, `R inferno' by Patrick Burns is recommended (https://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 R prompt 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, with
the demonstration capture history data `captdata':
traps(captdata) ## extraction method for `traps'
attributes(captdata) ## all attributes
Also, the function str provides a compact summary of any object:
str(captdata)
Claeskens, G. and Hjort N. L. (2008) Model Selection and Model Averaging. Cambridge: Cambridge University Press.