Description How do I install and start secr? How can I get help? How should I report a problem? Why do I get different answers from secr and Density? How can I speed up model fitting and model selection? Does secr use multiple cores? Can a model use detector-level covariates that vary over time? Things You Might Need To Know About R References
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.
Other required packages (MASS, nlme, stats) should be available as part of your R installation.
Like other contributed packages, secr needs to be loaded before
each use e.g.,
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:
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
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.
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
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.
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
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 fit more quickly when the covariate takes only a few different values.
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:
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
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:
Claeskens, G. and Hjort N. L. (2008) Model Selection and Model Averaging. Cambridge: Cambridge University Press.
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