I'm running an analysis in R with the Vegan package. It's really simple in the way that I only want the summary to extract some values. But it keeps telling me an error message. Why?
I have this dataset
feed.raw1 =structure(c(0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
5L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 7L, 11L, 3L, 1L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 3L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 8L, 7L, 5L, 1L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 10L, 5L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 5L, 0L, 0L, 8L, 9L, 0L, 0L, 5L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 15L, 0L,
51L, 10L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 3L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 45L, 203L, 17L, 54L, 4L, 1L, 0L, 0L, 0L, 0L, 10L,
9L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 12L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 22L, 206L, 9L, 16L, 1L,
1L, 6L, 6L, 0L, 0L, 4L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 7L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 12L, 3L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 23L, 4L, 1L, 2L, 0L, 2L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 76L, 0L, 96L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
11L, 0L, 3L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 270L,
144L, 7L, 8L, 15L, 6L, 6L, 2L, 6L, 1L, 25L, 5L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 14L, 2L, 1L, 0L, 0L, 0L, 0L,
0L, 3L, 0L, 0L, 0L, 3L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 7L, 0L, 0L, 0L, 0L, 0L,
0L, 14L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L,
0L, 0L), .Dim = c(12L, 32L), .Dimnames = list(c("a", "b", "c",
"d", "e", "f", "g", "h", "i", "j", "k", "l"), c("a", "b", "c",
"d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p",
"q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "a1", "b1",
"c1", "d1", "e1", "f1")))
And I'm running this analysis:
library(vegan)
feed_raw.hel = decostand(feed.raw1, method = "pa")
pca.feed=vegan::rda(feed_raw.hel, scale=FALSE)
head(summary(pca.feed))
It gives me this error:
Canonical correspondence analysis
Class: rda cca
Call: rda(X = feed_raw.hel, scale = FALSE)
Total inertia: 0
Eigenvalues:
Error in names(vec) <- paste("Ax", 1:length(vec), sep = "") :
attempt to set an attribute on NULL
No error found (see comments in the OP):
> library(vegan)
> feed_raw.hel = decostand(feed.raw1, method = "pa")
>
> pca.feed=vegan::rda(feed_raw.hel, scale=FALSE)
> head(summary(pca.feed))
Call:
rda(X = feed_raw.hel, scale = FALSE)
Partitioning of variance:
Inertia Proportion
Total 5.394 1
Unconstrained 5.394 1
Eigenvalues, and their contribution to the variance
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Eigenvalue 2.0696 0.7676 0.6639 0.5502 0.41578 0.31941 0.22209
Proportion Explained 0.3837 0.1423 0.1231 0.1020 0.07708 0.05922 0.04117
Cumulative Proportion 0.3837 0.5260 0.6491 0.7511 0.82817 0.88739 0.92856
PC8 PC9 PC10 PC11
Eigenvalue 0.15383 0.11310 0.07857 0.03984
Proportion Explained 0.02852 0.02097 0.01457 0.00739
Cumulative Proportion 0.95708 0.97805 0.99261 1.00000
Scaling 2 for species and site scores
* Species are scaled proportional to eigenvalues
* Sites are unscaled: weighted dispersion equal on all dimensions
* General scaling constant of scores: 2.775394
Species scores
PC1 PC2 PC3 PC4 PC5 PC6
a -0.03289 -0.13245 0.18066 0.12616 -0.2028751 0.07257
b -0.19170 -0.26686 -0.20142 -0.16621 0.0739356 -0.16726
c -0.43542 -0.24013 -0.02194 0.16668 -0.0037653 0.18018
d -0.43702 0.08614 -0.05548 -0.06814 -0.0009418 -0.03947
e -0.24815 -0.06070 0.29795 0.18439 -0.0879021 -0.02246
f 0.08852 0.11597 -0.07947 0.02250 -0.0926734 -0.13060
....
Site scores (weighted sums of species scores)
PC1 PC2 PC3 PC4 PC5 PC6
a -1.65813 0.55267 0.90341 0.4485 0.8856 -0.7321
b -1.70818 0.11084 -1.33080 -0.9734 -0.8929 0.4280
c -0.25333 -1.02024 1.39160 0.9718 -1.5627 0.5590
d -0.09478 -1.47685 -1.03494 1.1078 1.2228 -0.2536
e 0.26417 0.60502 0.71856 -0.6194 1.1614 1.2200
f 0.36048 -0.01608 -0.09826 -0.2709 0.3182 1.3866
....