On Wed, Sep 9, 2009 at 5:43 AM, Kim Vanselow<vanse...@gmx.de> wrote:
> 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.
Hi,
How about using something like modeled solar radiation instead of a
transformed aspect angle? It is fairly simple to do in a GIS (like
GRASS), and can give you much more information that does not come from
a circular distribution.
Cheers,
Dylan
> 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.simp...@ucl.ac.uk>
>> An: Kim Vanselow <vanse...@gmx.de>
>> CC: r-help@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
>> --
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>> 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
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>>
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