[R-sig-eco] Publication quality graphics in R

[Mark A. Albins]

R-sig-eco list,

This is a bit of a tangent from the current conversation, but can  
someone elaborate on
this quote from the following message,

"Plots in R come out so nicely, publication quality if you specify  
them correctly."

In particular, I'd like to hear from the list, how folks specify and  
export presentation
quality and publication quality graphics with R.  I've had problems  
when exporting
graphics using the copy-to-clipboard option (both bitmap and metafile)  
and also when
saving them as jpgs.  They almost always seem to look a little funny  
(e.g. pixelation,
symbols coming out distorted etc.).  The only option that I've had  
much success with is
saving them as pdf's, but that format is less than ideal when trying  
to incorporate a
graphic into another document (e.g. Word or Powerpoint), and is often  
not the format
requested by journals.

Any advice would be appreciated.

Thanks,

Mark

__________________________________________________

Message: 1
Date: Thu, 29 May 2008 20:21:54 -0400
From: Jessi Brown <jlbrown at unr.edu>
Subject: [R-sig-eco] AIC, R-Mark, and nest survival
To: r-sig-ecology at r-project.org
Message-ID: <483F48A2.9040906 at unr.edu>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

Hi, Dave. Thanks for pointing out the merits of R-Mark as far as
generating AIC tables reflecting the results of nest survival and other
data model types.

I do indeed use R-Mark for CJS and multistate population modeling, but I
prefer the logistic exposure/"Shaffer" nest modeling paradigm for a
number of reasons. When you have something of a background in linear
models, the GLM approach is perhaps a little more intuitive than Program
MARK (but R-Mark circumvents some of that), and data preparation and
covariate handling seems to go more quickly and easily. Plots in R come
out so nicely, publication quality if you specify them correctly. Also,
there's capacity for extending the logistic-exposure models to mixed
models (which might not be a wise decision, based on violation of the
assumption that the mean of the error distribution is equal to zero, but
I digress).

I've done nest survival with both Program MARK (not R-Mark) and GLMs in
R, and it seems to me (not a biostatistician, but an ecologist who
dabbles with statistical tools), that it's ok to just go with whatever
suits your particular style. In my case, since I tend to start with (and
retain) fairly focused, restricted model suites, it doesn't bother me
much to hand construct AIC tables with the "n-effective" calculated AIC
values after having run the GLMs.

BTW, if anyone needs a script of how to set up the logistic-exposure
link function, it's among the examples in help(family).

cheers, Jessi Brown

-- 
Jessi L. Brown
Ph.D. Student, Program in Ecology, Evolution, and Conservation Biology
University of Nevada, Reno

1000 Valley Rd.
Reno, NV 89512
jlbrown at unr.edu



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**************************************************
Mark A. Albins
Department of Zoology
Oregon State University
Corvallis, OR 97331-2914
phone:  (541) 740-7747
fax:     (541) 737-0501