similar to: Cannot find ldfortran (R on Cygwin)

Hi, I try to install vegan package on debian linux and I give this error: ================================================= * installing *source* package 'vegan' ... ** libs make: *** No rule to make target `cepin.o', needed by `vegan.so'. Stop. *** arch - Rgnome Warning in file(con, "r") : cannot open file '/usr/lib/R/etc/Rgnome/Makeconf': No such file or

Hi again, I've just uninstalled R2.01 and installed the new R2.10 on my WindowsXP machine. I then attempted to install the vegan package from source files, as I learned to do last week, with the help of some of you. I have updated my path variable to the new R directory (R/rw2010/bin instead of R/rw2001pat/bin), and I've still got the HTMLHelpWorkshop files installed, as well as Perl

Hello, I can draw a basic stress plot for NMDS with the following code in package Vegan. > stressplot(parth.mds, parth.dis) When I try to specify the line and point types, it gives me error message. > stressplot(parth.mds, parth.dis, pch=1, p.col="gray", lwd=2, l.col="red") Error in plot.xy(xy, type, ...) : invalid plot type In the above code, if I removed line type,

Dear All, I was wondering whether anyone might be able to provide some advice with an nMDS / R problem. I?m trying to run nMDS on a dataset that contains many missing values and was wondering how I can account for the missing values when running nMDS? It seems as though the data are being grouped depending on where the zero values appear. Any suggestions greatly appreciated. Thank you very much

Are you missing values coded as missing values? That is, are they NA? Such NA-values should be handled by vegan::monoMDS. Naturally, it can be possible that your data set cannot be meaningfully analysed if you have too many missing values or missing values are grouped in peculiar ways. Zero-values are not missing values, but they indicate 0-difference (= identical observations). The missing values

I'm not sure how you are incorporating time period into your data structure. Typically we are looking at plots or assemblages as the rows and taxa as the columns. Time period adds a third dimension that could be added as blocks of rows. For example, depending on the resolution of your data, one approach would be to have up to 8 rows for each locality: Loc1.1000, Loc1.2000, . . . Loc1.8000. If