Shep.comp {asbio} | R Documentation |
Calculates raw R^2, Linear fit R^2 and non-metric R^2 for PCoA ordinations.
Shep.comp(dis, max.dim = 10)
dis |
A dissimilarity matrix. Any index from get.dist can be used. |
max.dim |
The maximum number of cmdscale dimensions for which correlations should be calculated. |
The function Shep.comp in asbio creates calculates three sorts correlations coefficients across all specified PCoA dimensionalities.
1) Pearson's correlations of observed distances (in the distance matrix) and fitted distances (in a PCoA ordination); i.e. "raw fits."
2) Pearson's correlations between the monotonic fitted line and the observed distances "linear fits".
3) A correlation based on stress and calculated as 1 - S^2 (Oksanen 2008), where:
S = sqrt(sum(d.hat[ij]-d[ij])^2/sum[ij]^2)
where hat{d}_{ij} is the distance between rank order of distance between point i and j in the final configuration (i.e. the fitted monotonic stressplot line), and d_{ij} is the distance between point i and j in the original distance matrix.
The first type of correlation "raw fits" should probably not be used since the relationship between community dissimilarity and a configuration may be strong while being non-linear. The second correlation should be linear even if the relationship between observed dissimilarity and dissimilarities in the final configuration are non-linear, and is often referred to as the linear fit (Oksanen 2008). The final type of correlation has been called "non-metric" fit (Oksanen 2008).
Returns a matrix with three columns containing R^2 for raw, linear, and non-metric R^2's.
Ken Aho
Oksanen, J. (2008) Multivariate analysis of ecological communities in R: vegan tutorial. http://cc.oulu.fi/~jarioksa/opetus/metodi/vegantutor.pdf
data(varespec) d<-get.dist(varespec,"steinhaus") Shep.comp(d,max.dim=10)