Thank you Baptiste and Jim- I look forward to trying these ideas out
when I have a chance.
Mike
baptiste auguie wrote:
Hi,
>From what I understand of your code, you might find the following
construct useful,
funs <- c("mean", "sum", "sd", "diff")
x <- 1:10
lapply(funs, do.call, args=list(x))
Hi,
>From what I understand of your code, you might find the following
construct useful,
funs <- c("mean", "sum", "sd", "diff")
x <- 1:10
lapply(funs, do.call, args=list(x))
and then working with lists rather than naming every object
individually. You might find mapply useful too when you have t
Here is a hint of how you might want to do the first part. You might
want to study the 'lappy' function
vegList <- lapply(c('bray', ..., 'binomial'), function(.method){
vegdist(x, method=.method)
})
clustList <- lapply(vegList, function(.dist){
lapply(c('average', ..., 'centroid'), funct
Dear All,
I wrote a function for cluster analysis to compute cophenetic correlations
between dissimilarity matrices (using the VEGAN library) and cluster
analyses of every possible clustering algorithm (SEE ATTACHED)
http://old.nabble.com/file/p26288610/cor.coef.R cor.coef.R . As it is now,
it i
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