[R] NA in cca (vegan)

[Gavin Simpson]

On Fri, 2009-09-04 at 17:15 +0200, Kim Vanselow wrote:
> Dear all,
> I would like to calculate a cca (package vegan) with species and
> environmental data. One of these environmental variables is
> cos(EXPOSURE).
> The problem: for flat releves there is no exposure. The value is
> missing and I can't call it 0 as 0 stands for east and west.
> The cca does not run with missing values. What can I do to make vegan
> cca ignoring these missing values?
> Thanks a lot,
> Kim

Hi Kim,

This is timely as Jari Oksanen (lead developer on vegan) has been
looking into making this happen automatically in vegan ordination
functions. The solution for something like cca is very simple but it
gets more complicated when you might like to allow features like
na.exclude etc and have all the functions that operate on objects of
class "cca" work nicely.

For the moment, you should just process your data before it goes into
cca. Here I assume that you have two data frames; i) Y is the species
data, and ii) X the environmental data. Further I assume that only one
variable in X has missings, lets call this Exposure:

## dummy data
set.seed(1234)
## 20 samples of 10 species
Y <- data.frame(matrix(rpois(20*10, 2), ncol = 10))
## 20 samples and 5 env variables
X <- data.frame(matrix(rnorm(20*5), ncol = 5))
names(X) <- c(paste("Var", 1:4, sep = ""), "Exposure")
## simulate some NAs in Exposure
X$Exposure[sample(1:20, 3)] <- NA
## show X
X

## Now create a new variable indicating which are missing
miss <- with(X, is.na(Exposure))

## now create new X and Y omitting these rows
Y2 <- Y[!miss, ]
X2 <- X[!miss, ]

## Now submit to CCA
mod <- cca(Y2 ~ ., data = X2)
mod

## plot it
plot(mod, display = c("sites","bp"), scaling = 3)

## It'd be nice to get predictions for the 3 samples we missed out
pred <- predict(mod, newdata = Y[miss, ], type = "wa", scaling = 3)

## add these points to the ordination:
points(pred[, 1:2], col = "red", cex = 1.5)

HTH

G
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