[R] "predict"-fuction for metaMDS (vegan)

[Kim Vanselow]

Dear r-Community,
Step1: I would like to calculate a NMDS (package vegan, function metaMDS) with species data.
Step2: Then I want to plot environmental variables over it, using function envfit.
The Problem: One of these environmental variables is cos(EXPOSURE). But 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. Therefore I kicked all releves with missing environmental variables. Both, metaMDS and envfit then work without problems.
Now I want to bring the releves with missing environmetal variables back to my ordination-plot.

Gavin Simpson gave me the advice to use the predict-function for the same missing value problem when I was calculating a cca. This worked without problem.

As my species data was recorded in Braun-Blanquet-numbers (ordinal scale) I would prefer to calculate a NMDS. Does anybody know a similar function to the predict function which works with NMDS or does anybody know how to modify the predict function so that it will work also for NMDS?

Thank you very much!

Kim
 
-------- Original-Nachricht --------
> Datum: Fri, 04 Sep 2009 18:11:09 +0100
> Von: Gavin Simpson <gavin.simpson at ucl.ac.uk>
> An: Kim Vanselow <Vanselow at gmx.de>
> CC: r-help at r-project.org
> Betreff: Re: [R] NA in cca (vegan)

> 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
> --
> %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
>  Dr. Gavin Simpson             [t] +44 (0)20 7679 0522
>  ECRC, UCL Geography,          [f] +44 (0)20 7679 0565
>  Pearson Building,             [e] gavin.simpsonATNOSPAMucl.ac.uk
>  Gower Street, London          [w] http://www.ucl.ac.uk/~ucfagls/
>  UK. WC1E 6BT.                 [w] http://www.freshwaters.org.uk
> %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
-- 
Jetzt kostenlos herunterladen: Internet Explorer 8 und Mozilla Firefox 3 -
sicherer, schneller und einfacher! http://portal.gmx.net/de/go/atbrowser