makecwm: Community-weighted means (CWM)

Description Usage Arguments Details Value References Examples

Description

Community-weighted means traits matrix (SU x traits)

Usage

1
2
makecwm(spp, trait, wa = TRUE, stdz = c("minmax", "deviates", "none"),
  ...)

Arguments

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

Details

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.

Value

A community-weighted traits matrix (actually a data frame), where rows=SUs and cols=traits.

References

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.

Examples

 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)

phytomosaic/ecole documentation built on May 6, 2019, 3:45 p.m.