NMDS in vegan
Quoting Manuel Sp?nola <mspinola10 at gmail.com>:
Dear list, I am trying to run a NMDS in vegan. I am using standardized variables, so I have negative values. This is my output:
metricas =read.table("metricas_std.txt", header=TRUE)
attach(metricas)
The following object(s) are masked from metricas ( position 3 ) :
LPI MNN MSI NUMP
metricas
NUMP LPI MSI MNN 1945 -1.72 0.10 -1.20 -1.03 1960 0.83 0.62 -0.90 -1.13 1980 0.55 1.27 0.12 0.45 1997 0.09 -1.00 0.90 0.97 2005 0.24 -1.00 0.72 0.72
metricas.dis <- vegdist(metricas, "euclidean") metricas.dis
1945 1960 1980 1997
1960 2.6216216 1980 3.2333574 2.0094029
1997 3.5911140 3.2896808 2.4986596 2005 3.4351856
3.0032316 2.3836736 0.3426368
metamds = metaMDS(metricas, distance="euclidean")
Using step-across dissimilarities: Too long or NA distances: 9 out of 10 (90.0%) Stepping across 10 dissimilarities... Error in isoMDS(dist, k = k, trace = isotrace) : an initial configuration must be supplied with NA/Infs in 'd' In addition: Warning message: In stepacross(dis, trace = trace, ...) : Disconnected data: Result will contain NAs Do you know what is going on?
Only what metaMDS says above: you use the default of regarding
dissimilarities >1 as NA (missing), and try to use 'stepacross' to
replace those. However, only one dissimilarity is left ("Too long or
NA distances: 9 out of 10 (90.0%)") which leaves you one observed
value. With this you cannot reconstitute a complete dissimilarity
matrix, and you get "Disconnected data: Result will contain NAs" and
in this case "an initial configuration must be supplied with NA/Infs
in 'd'". That wouldn't help with only one observed dissimilarity,
though.
metaMDS is intended for ordinary community data which you don't have.
You have to turn off those default options that are used for community
data. In this case that means:
metaMDS(x, "euc", too=0, wa=FALSE, auto=FALSE)
Here auto=F is not strictly necessary, but it is safe for you kind of data.
This gives you zero stress which indicates that in this case (and with
five points!) metaMDS/isoMDS is a dubious choice.
cheers, jari oksanen