Description Usage Arguments Details Value References Examples
Community-weighted means traits matrix (SU x traits)
1 |
spp |
array of species data, where rows=SUs, cols=species |
trait |
array of traits data, where rows=species, cols=traits |
wa |
logical, should weighted averaging step follow standardization step? If TRUE, then use abundance weighted trait AVGS, if FALSE, then use abundance weighted trait TOTALS. |
stdz |
standardize each trait by its 'minmax' (default), 'deviates', or 'none' |
... |
further arguments passed to other methods |
Behavior emulates PC-ORD; see also McCune and Grace (2002). Recommend not changing default wa and stdz settings, as these create the CWM matrix that is sensible for most other analyses.
A community-weighted traits matrix (actually a data frame), where rows=SUs and cols=traits.
McCune, B., and J. B. Grace. 2002. Analysis of Ecological Communities. MjM Software, Gleneden Beach, Oregon, USA. 304 pp.
McCune, B. 2015. The front door to the fourth corner: variations on the sample unit x trait matrix in community ecology. Community Ecology 16:267-271.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # following Fig. 2 in McCune (2015):
A <- data.frame(t(matrix(c(4,0,0,2,1,1,1,5,2,0,3,4),
nrow=3, ncol=4)))
dimnames(A)[[1]] <- paste0('Plot',1:4)
dimnames(A)[[2]] <- c('maple', 'oak', 'pine')
S <- data.frame(t(matrix(c(10,1,2,1,1,0),
nrow=2, ncol=3)))
dimnames(S)[[1]] <- c('maple', 'oak', 'pine')
dimnames(S)[[2]] <- c('shadetol', 'hardwood')
# trts standardized by none, abund-weighted totals
c1 <- makecwm(A, S, wa=FALSE, stdz='none')
# traits standardized by minmax, abund-weighted totals
c2 <- makecwm(A, S, wa=FALSE, stdz='minmax')
# traits standardized by std deviates, abund-weighted totals
c3 <- makecwm(A, S, wa=FALSE, stdz='deviates')
# traits standardized by none, abund-weighted averages
c4 <- makecwm(A, S, wa=TRUE, stdz='none')
# traits standardized by minmax, abund-weighted averages
c5 <- makecwm(A, S, wa=TRUE, stdz='minmax') # PREFERRED!
# traits standardized by std deviates, abund-weighted averages
c6 <- makecwm(A, S, wa=TRUE, stdz='deviates')
# print values
list(c1,c2,c3,c4,c5,c6)
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