Hello everyone,

This is my first post in this list and I am a new r user and I am not very
experienced in multivariate analysis, sorry if my question is silly.

I am trying to run a cca (vegan package) but I am encountering a problem: I
want to constrain the analysis by all environment variables, but it do not
happens. The program "choose" some of it. When I try to plot the results
only these "automatic chosen" variables are ploted. I tried to run cca with
vegan data (varespec and varechem) and it worked fine.

There is something strange on the "Biplot scores for constraining
variables" results. The CA's have only 0 values (bellow in yellow). I
am writing the complet "Biplot scores for constraining variables" section.

My data is like this:

I have 9 samples (I will not show all data to reduce the email size).

I have 9 environmental variables (nutrients).
> env
      Depth   Sal Nitrite Nitrate Amonia T_Org_N Silicate Total_I_P OrthoP
Vit1     63 37.07    0.03    1.24  0.031   5.849     0.85      0.14   0.08
Vit2     63 37.07    0.03    1.24  0.031   5.849     0.85      0.14   0.08
Dav1     40 37.35    0.05    1.43  0.031   8.849     0.76      0.11   0.14
Dav2     40 37.35    0.05    1.43  0.002   8.878     0.76      0.11   0.14
.
.
.

I have 32 "species" (actually this is bacterial and archeal phyla). I will
not show all columns to reduce the email size.

> spe
      Acidobacteria Actinobacteria Aquificae ...
Vit1              9            129         5      ...
Vit2             14            154         8      ...
Dav1              8            122         4      ...


I am running the cca with the follow script:

>library(vegan)
>spe <- read.csv("cvt_org_test.csv", row.names=1)
>env <- read.csv("test_chem2.csv", row.names=1)
> spe.hel <- decostand(spe, "hellinger")
> spe.cca <- cca(spe.hel,env)
> summary(spe.cca) # Scaling 2 (default)
Call:
cca(X = spe.hel, Y = env)

Partitioning of mean squared contingency coefficient:
              Inertia Proportion
Total         0.06054     1.0000
Constrained   0.03120     0.5154
Unconstrained 0.02934     0.4846

Eigenvalues, and their contribution to the mean squared contingency
coefficient

Importance of components:
                         CCA1     CCA2     CCA3     CCA4     CA1      CA2
  CA3      CA4
Eigenvalue            0.01906 0.006717 0.003055 0.002371 0.01433 0.007767
0.00482 0.002418
Proportion Explained  0.31480 0.110960 0.050470 0.039170 0.23673 0.128300
0.07963 0.039940
Cumulative Proportion 0.31480 0.425760 0.476230 0.515400 0.75213 0.880430
0.96006 1.000000

Accumulated constrained eigenvalues
Importance of components:
                         CCA1     CCA2     CCA3     CCA4
Eigenvalue            0.01906 0.006717 0.003055 0.002371
Proportion Explained  0.61079 0.215280 0.097920 0.076010
Cumulative Proportion 0.61079 0.826070 0.923990 1.000000

Scaling 2 for species and site scores
* Species are scaled proportional to eigenvalues
* Sites are unscaled: weighted dispersion equal on all dimensions


Species scores

                         CCA1      CCA2      CCA3      CCA4        CA1
   CA2
Acidobacteria        0.065258  0.038704  0.009574 -0.112006  0.0817235
 0.0143286
Actinobacteria       0.077451  0.049255 -0.046415  0.045290  0.0178117
-0.0231979
Aquificae            0.137472 -0.057698  0.017360 -0.017047  0.0859391
-0.0749676
.
.
.

Site scores (weighted averages of species scores)

          CCA1    CCA2    CCA3      CCA4      CA1      CA2
Vit1  -0.35189 -0.7507 -2.6425 -0.961233 -0.72976  0.08807
Vit2   0.04988 -0.2552 -1.0121  0.321500  0.66991 -0.08085
.
.
.

Site constraints (linear combinations of constraining variables)

         CCA1    CCA2    CCA3    CCA4      CA1      CA2
Vit1  -0.1424 -0.4923 -1.7924 -0.2924 -0.72976  0.08807
Vit2  -0.1424 -0.4923 -1.7924 -0.2924  0.66991 -0.08085
.
.
.

Biplot scores for constraining variables

           CCA1    CCA2    CCA3     CCA4 CA1 CA2
Depth    0.6576 -0.3701 -0.6555 -0.02914   0   0
Sal     -0.6385  0.3949  0.6603  0.01846   0   0
Nitrite  0.5303  0.7679  0.1164 -0.33999   0   0
Amonia  -0.9195  0.1331  0.3570  0.09710   0   0


Thank you very much for your help and support!

All the best!

-- 
_________________________________________
Pedro Milet Meirelles
www.flickr.com/pedromeirelles

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