Hello everyone,
I have inconsistent results using vegan's capscale() and rda() on a hellinger
distance matrix, based on presence/absence data matrix of 192 plants species
(columns) x 70 sites (rows).
HellingerFloreDist <-dist(decostand(flore[12:204], method="hellinger"))
I first wanted to perform a Canonical Analysis of Principal coordinates (CAP)
using capscale() function. However, based on Jari Oksanen comment, i.e.
"Constrained Analysis of Principal Coordinates (CAP) is an ordination method
similar to Redundancy Analysis (rda)... If called with Euclidean distance, the
results are identical to rda, but capscale will be much more inefficient.", I
decided to perform RDA using rda().
###CAP analysis
CAP<-capscale(HellingerFloreDist~ancien+mature+interact, data=data)
anova(CAP) #Test of the significance of the analysis
anova(CAP, by="axis", perm.max=999) #test axes for significance
anova(CAP, by="terms", permu=999) #test environment variables for significance
RsquareAdj(CAP)
summary(CAP)
###RDA analysis
RDA<-rda(flore.hellinger~ancien+mature+interact, data=data)
plot(RDA)
anova(RDA) #Test of the significance of the analysis
anova(RDA, by="axis", perm.max=999) #test axes for significance
anova(RDA, by="terms", permu=999) #test environment variables for significance
RsquareAdj(RDA)
summary(RDA)
However, I was quite surprised by the results, i.e. the difference between the
total inertia of CAP and RDA.
Results from CAP (capscale) :
Partitioning of squared Euclidean distance:
Inertia Proportion
Total 35.957 1.00000
Constrained 2.052 0.05706
Unconstrained 33.905 0.94294
Results from RDA :
Partitioning of variance:
Inertia Proportion
Total 1.5384 1.00000
Constrained 0.1096 0.07124
Unconstrained 1.4289 0.92876
Searching all day long for an explanation, I still don't understand those
results.
Any explanation would be greatly appreciated.
Thanks in advance.
Philippe JANSSEN
Doctorant - UR Ecosyst�mes Montagnards
Irstea - Centre de Grenoble
+33 (0)4 76 76 28 79
Philippe.janssen @irstea.fr
www.irstea.fr <http://www.irstea.fr/>
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