Hello,
I am running an ENFA analysis and am having trouble interpreting the
specialization values. Do the specialization values correspond to each
ecogeographical variable? If so they don't make sense in terms of what I am
seeing for the marginality values, i.e. marginality value for water is high and
displays a long arrow on the biplot. Therefore, I would think that the
specialization value for water would be high, but if the vector of
specialization values corresponds to each ecogeographical variable then this is
not the case. Am I missing a line of code? An explanation of how to interpret
the specialization values from the code and output below would be much
appreciated.
slot(maps,"data")[,1:9] <- sqrt(slot(maps,"data")[,1:9])
hist(maps, type = "l")
## Prepare the data for the ENFA
tab <- slot(maps, "data")
pr <- slot(count.points(pipl, maps), "data")[,1]
## Perform the PCA before the ENFA
pc <- dudi.pca(tab, scannf = FALSE)
pc
(enfa1 <- enfa(pc, pr,scannf = FALSE))
enfa1$s #gives specialization values
enfa1$mar#gives marginality values
##OUTPUT##
> (enfa1 <- enfa(pc, pr,scannf = FALSE))
ENFA
$call: enfa(dudi = pc, pr = pr, scannf = FALSE)
marginality: 1.731
eigen values of specialization: 6.61 2.583 1.924 1.49 1.153 ...
$nf: 1 axis of specialization saved
vector length mode content
1 $pr 385923 numeric vector of presence
2 $lw 385923 numeric row weights
3 $cw 10 numeric column weights
4 $mar 10 numeric coordinates of the marginality vector
5 $s 9 numeric eigen values of specialization
data.frame nrow ncol content
1 $tab 385923 10 modified array
2 $li 385923 2 row coordinates
3 $co 10 2 column coordinates
> enfa1$s #gives specialization values
[1] 6.6101077 2.5830759 1.9242150 1.4895781 1.1533000 1.0651184 0.8456433
0.7114272 0.4710257
> enfa1$mar #gives marginality values
Annual Perennial Grassland Deciduous
Coniferous
0.38442513 0.33468805 0.62931566 -0.31320996
-0.55314134
Mixed Water Wetland Shrub
Distance_to_Roads
-0.53532160 0.55172030 -0.25751795 0.02533396
-0.11602451
Thank you
~Ruth
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