Hello R experts and Vegan users, I am re-posting my msg as I didn't get any reply. I am not sure if it was not sent properly. Please help. Sorry for repeated post.
I want to do ordination plots using vegan metaMDS. I have a where many cells have zero values. Data structure: X[1:10,1:14] Height.1 Height.2 Height.3 Height.4 Height.5 Height.6 Height.7 Height.8 Height.9 Height.10 Height.11 Height.12 Height.13 D30I1A 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1B 46 0 0 0 0 0 0 0 0 0 39 0 98 D30I1C 70 0 0 0 0 0 0 0 0 0 0 85 0 D30I2A 47 0 0 0 0 0 0 0 0 0 49 0 105 D30I2B 68 0 0 0 0 0 0 0 0 0 83 0 214 D30I2C 0 75 0 0 0 0 0 0 0 0 0 83 0 D30I3A 48 0 0 0 0 0 0 0 0 0 42 0 107 D30I3B 64 0 0 0 0 0 0 0 0 0 72 0 177 D30I3C 72 0 0 0 0 0 0 0 0 0 0 96 0 D30M1A 60 0 0 0 0 0 0 0 0 0 74 0 169 When I tried to perform metaMDS, it was not working, with the error > ord1 <- metaMDS( X ="bray") Square root transformation Wisconsin double standardization Error in if (any(dist < -sqrt(.Machine$double.eps))) warning("some dissimilarities are negative -- is this intentional?") : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In distfun(comm, method = distance, ...) : you have empty rows: their dissimilarities may be meaningless in method bray 2: In distfun(comm, method = distance, ...) : missing values in results I realised some of the values are either zero or similar. I tried to check it with distance X .dist <- metaMDSdist( X , method="bray") got the error: Square root transformation Wisconsin double standardization Error in distfun(comm, method = distance, ...) : formal argument "method" matched by multiple actual arguments When I checked the distance I see some of the distances are NaN as for example some rows of dist matrix: D3M1A NaN 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 NaN 1.000000000 1.000000000 NaN :::: D3R1A NaN 1.000000000 1.000000000 1.000000000 1.000000000 1.000000000 NaN 1.000000000 1.000000000 NaN I searched all the details of metaMDS where it is suggested to avail the argument 'zerodist' So I tried both X.dist1 <- metaMDSdist(X, method="bray",zerodist = "ignore") X.dist2 <- metaMDSdist(X, method="bray",zerodist = "add") But for both the cases I still have those NaN values in some distances. I have read, one of the discussion says modify zero dissimilarities as: If there is a good reason, and you want to include all samples, then you'll need to come up with a means for handling them. metaMDSdist allow you to add a small value to the zero dissimilarities. The details are in the code, but effectively all zero distances are replaced by half the smallest non zero distance. You could do a similar replacement yourself if you feel this is warranted and/or justified. minDij <- min(Dij[Dij > 0) / 2 Dij[Dij <= 0] <- minDij But still I don't understand how can I modify the NaN values. In my data I don't have any NA values. All the cells are either +ve or zero. Please help me with this. Should I just replace all the NaN values with zero? Please advice. Thanks a lot, Mitra [[alternative HTML version deleted]]
______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.