Hi there,
I have a dataset with 26 observations and 7039 variables each. The data looks like this
| SampleID |
Time |
Prot1 |
Prot2 |
Prot3 |
Prot4 |
Prot5 |
Prot6 |
| t0_2 |
Start |
0.004898333 |
0.000986609 |
0 |
0 |
0.020992855 |
0 |
| t0_3 |
Start |
0.006429024 |
0 |
0 |
0 |
0.025716094 |
0 |
| t0_5 |
Start |
0.003940074 |
0.000925866 |
0.000990521 |
0 |
0.015760296 |
0.001959153 |
with Prot1 - Prot7039. Missing data has been replaced with 0. To visualize and test my data, I want to conduct a NMDS using the vegan package. However, when I run the NMDS I get an error saying
*** Solution reached
Warning message:
In metaMDS(data_1, distance = "bray", k = 2) :
stress is (nearly) zero: you may have insufficient data
The stress values is usually around 9.873474e-05. These are the commands I ran
#Load the data
Exp245 <- read.table("Exp245_NMDS_Input.txt", header = TRUE)
#Create subsets for the NMDS command
data_1 <- Exp245[,3:7039]
data_2 <- Exp245[,1:2]
#Load vegan
library(vegan)
#Run the actual NMDS analysis
NMDS <- metaMDS(data_1, distance = "bray", k = 2)
I did this analysis with a similar dataset and it worked perfectly fine. I tried to transform the data hoping that this will change something but it didn't. Do you have a solution for this? Should I maybe try another distance matrix? Or is it because there are so many 0 in the dataset which makes the matrix rank insufficient?
Thank you all in advance!
Cheers,
Marlene