Publication quality graphics in R
Hi Mark, I second writing to files as David says, but I have a few things to add: - jpg is meant to encode photos, and because of the compression it uses it will butcher complicated text, especially if you have to re-save multiple times, resize the images, etc... PNG avoids this problem but still compresses photo-like images nicely (say colored 3-D plots). MS Office will definitely accept PNG. - postscript is probably best for simpler figures (I think MS Office accepts it happily) and journals should be happy to take it (?) - when you open the PNG/pdf/postscript device for writing an image, it helps to already know what size/resolution the image needs to be because resizing will almost certainly alter the look of text. I tend to save data frames for making particular figures instead of saving images for this reason. - I haven't had trouble with this, but the ?postscript help page mentions that if you use complex symbols, you need to make sure you have good fonts/encodings set for them--see the encodings section on ?postscript... - The R wiki has some good information on preparing images. Hope that helps, Krzysztof
Mark A. Albins wrote:
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