eigenvals.RdFunction extracts eigenvalues from an object that has them. Many multivariate methods return such objects.
eigenvals(x, ...) # S3 method for cca eigenvals(x, model = c("all", "unconstrained", "constrained"), constrained = NULL, ...) # S3 method for eigenvals summary(object, ...)
| x | An object from which to extract eigenvalues. |
|---|---|
| object | An |
| model | Which eigenvalues to return for objects that inherit from class
|
| constrained | Return only constrained eigenvalues. Deprecated as of vegan
2.5-0. Use |
| ... | Other arguments to the functions (usually ignored) |
This is a generic function that has methods for cca,
wcmdscale, pcnm, prcomp,
princomp, dudi (of ade4), and
pca and pco (of
labdsv) result objects. The default method also
extracts eigenvalues if the result looks like being from
eigen or svd. Functions
prcomp and princomp contain square roots
of eigenvalues that all called standard deviations, but
eigenvals function returns their squares. Function
svd contains singular values, but function
eigenvals returns their squares. For constrained ordination
methods cca, rda and
capscale the function returns the both constrained and
unconstrained eigenvalues concatenated in one vector, but the partial
component will be ignored. However, with argument
constrained = TRUE only constrained eigenvalues are returned.
The summary of eigenvals result returns eigenvalues,
proportion explained and cumulative proportion explained. The result
object can have some negative eigenvalues (wcmdscale,
capscale, pcnm) which correspond to
imaginary axes of Euclidean mapping of non-Euclidean distances
(Gower 1985). In these cases, the sum of absolute values of
eigenvalues is used in calculating the proportions explained, and
real axes (corresponding to positive eigenvalues) will only explain
a part of total variation (Mardia et al. 1979, Gower 1985).
An object of class "eigenvals", which is a vector of
eigenvalues.
The summary method returns an object of class
"summary.eigenvals", which is a matrix.
Gower, J. C. (1985). Properties of Euclidean and non-Euclidean distance matrices. Linear Algebra and its Applications 67, 81--97.
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Chapter 14 of Multivariate Analysis, London: Academic Press.
#> CCA1 CCA2 CCA3 CA1 CA2 CA3 CA4 CA5 #> 0.3615566 0.1699600 0.1126167 0.3500372 0.2200788 0.1850741 0.1551179 0.1351054 #> CA6 CA7 CA8 CA9 CA10 CA11 CA12 CA13 #> 0.1002670 0.0772991 0.0536938 0.0365603 0.0350887 0.0282291 0.0170651 0.0122474 #> CA14 CA15 CA16 CA17 CA18 CA19 CA20 #> 0.0101910 0.0094701 0.0055090 0.0030529 0.0025118 0.0019485 0.0005178summary(ev)#> Importance of components: #> CCA1 CCA2 CCA3 CA1 CA2 CA3 CA4 #> Eigenvalue 0.3616 0.16996 0.11262 0.3500 0.2201 0.18507 0.15512 #> Proportion Explained 0.1736 0.08159 0.05406 0.1680 0.1056 0.08884 0.07446 #> Cumulative Proportion 0.1736 0.25514 0.30920 0.4772 0.5829 0.67172 0.74618 #> CA5 CA6 CA7 CA8 CA9 CA10 CA11 #> Eigenvalue 0.13511 0.10027 0.07730 0.05369 0.03656 0.03509 0.02823 #> Proportion Explained 0.06485 0.04813 0.03711 0.02577 0.01755 0.01684 0.01355 #> Cumulative Proportion 0.81104 0.85917 0.89627 0.92205 0.93960 0.95644 0.96999 #> CA12 CA13 CA14 CA15 CA16 CA17 #> Eigenvalue 0.017065 0.012247 0.010191 0.009470 0.005509 0.003053 #> Proportion Explained 0.008192 0.005879 0.004892 0.004546 0.002644 0.001465 #> Cumulative Proportion 0.978183 0.984062 0.988954 0.993500 0.996145 0.997610 #> CA18 CA19 CA20 #> Eigenvalue 0.002512 0.0019485 0.0005178 #> Proportion Explained 0.001206 0.0009353 0.0002486 #> Cumulative Proportion 0.998816 0.9997514 1.0000000## choose which eignevalues to return eigenvals(mod, model = "unconstrained")#> CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 #> 0.3500372 0.2200788 0.1850741 0.1551179 0.1351054 0.1002670 0.0772991 0.0536938 #> CA9 CA10 CA11 CA12 CA13 CA14 CA15 CA16 #> 0.0365603 0.0350887 0.0282291 0.0170651 0.0122474 0.0101910 0.0094701 0.0055090 #> CA17 CA18 CA19 CA20 #> 0.0030529 0.0025118 0.0019485 0.0005178