On Tue, 2011-09-13 at 13:46 +0800, 'Ben Ford' wrote: > Hi all, > > I am fairly new to R and I am trying to run mvpart and create a MRT using > explanatory variables and covariables. I've been following the procedures in > Numerical Ecoogy with R. > > The command (no covariables) which works fine - > > ABUNDTMRT <- mvpart(abundance ~ > .,factors,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4) > > where abundance is 4th root transformed fish abundance (103 species x 168 > samples), and factors is the relief (high, medium, low profile, sand > inundated reef, flat), benthos (coral, sessile inverts, kelp, macroalgae, > seagrass, sand), depth (continuous in meters), latitude, and longitude of > each sample. > > To try and incorporate spatial autocorrelation (as a covariate) into this I > have been trying the command - > > ABUNDTMRT <- mvpart(abundance ~ environ + spatial, > data.frame,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4)
I really don't think you can do that, and even if you could it is not a good thing to do. Arrange your variables (explanatory or covariables) into a single data frame, then, if this data frame contains only variable that go into the model (explanatory or covariables), you can use the `.` notation: ABUNDTMRT <- mvpart(abundance ~ ., data = my.data.frame, ...) However, if you are using covariable in the sense of Canoco, then I think you are out of luck. There isn't a way to partial out the spatial effects and fit a model using the non-spatial components of the environmental/explanatory variables. I wonder where you got the impression that this could be done? Of course, you can do this (sort of) if you are prepared to do a bit of work; i) ordinate the abundance data with spatial covariables as constraints and take the residuals (all site scores in unconstrained axes), ii) do the the same thing with the environment so that you residualise the explanatory variables, iii) take the residualised species data and the residualised explanatory matrix and use them as inputs to mvpart(). There may be infelicities here if you need to use different ordination techniques to residualise the species and the explanatory variables matrices... HTH G > where abundance is as above, environ is the environmental factors (from > above) and spatial is the eigenfunctions from a PCNM analysis. data.frame is > the environ and spatial factors as a data.frame. > > This gives the error - > > "Error in `[[<-.data.frame`(`*tmp*`, preds, value = c(72L, 72L, 80L, 72L, > : > replacement has 504 rows, data has 168" > > As I am new to this, I am not sure if I am entering an incorrect formula > when trying to include the covariables, or if this is just something which > mvpart cannot do. > > Thanks. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@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. -- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% 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@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.
