Weighted Averages Scores for Species
Computes Weighted Averages scores of species for ordination configuration or for environmental variables.
wascores(x, w, expand=FALSE) eigengrad(x, w)
x |
Environmental variables or ordination scores. |
w |
Weights: species abundances. |
expand |
Expand weighted averages so that they have the same weighted variance as the corresponding environmental variables. |
Function wascores computes weighted averages. Weighted averages
“shrink”: they cannot be more extreme than values used for
calculating the averages. With expand = TRUE, the function
“deshrinks” the weighted averages by making their biased
weighted variance equal to the biased weighted variance of the
corresponding environmental variable. Function eigengrad
returns the inverses of squared expansion factors or the attribute
shrinkage of the wascores result for each environmental
gradient. This is equal to the constrained eigenvalue of
cca when only this one gradient was used as a
constraint, and describes the strength of the gradient.
Function wascores returns a matrix where species define rows
and ordination axes or environmental variables define columns. If
expand = TRUE, attribute shrinkage has the inverses of
squared expansion factors or cca eigenvalues for the
variable. Function eigengrad returns only the shrinkage
attribute.
Jari Oksanen
data(varespec) data(varechem) vare.dist <- vegdist(wisconsin(varespec)) vare.mds <- monoMDS(vare.dist) vare.points <- postMDS(vare.mds$points, vare.dist) vare.wa <- wascores(vare.points, varespec) plot(scores(vare.points), pch="+", asp=1) text(vare.wa, rownames(vare.wa), cex=0.8, col="blue") ## Omit rare species (frequency <= 4) freq <- apply(varespec>0, 2, sum) plot(scores(vare.points), pch="+", asp=1) text(vare.wa[freq > 4,], rownames(vare.wa)[freq > 4],cex=0.8,col="blue") ## Works for environmental variables, too. wascores(varechem, varespec) ## And the strengths of these variables are: eigengrad(varechem, varespec)
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