[R-sig-eco] 3d fitting plane
Hi folks, I look for a fast way to estimate a 3d fitting plane to my 3d data. I do not want z~y+x as this is a regression model. I just want the equation of the best plane that fits the data in 3d. Maybe using princomp() and Total least squares? Looking around I found some solutions but nothing definitive. Thanks in advance for any suggestion. best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] 3d fitting plane
I tried using prcomp(): library(compositions) library(rgl) x <- rnorm(100) y <- rnorm(100) z <- rnorm(100) mat<-cbind(x,y,z) plot3D(mat,col=3,bbox=F) pr<-prcomp(mat) planes3d(pr$rotation[3,1]*sign(pr$rotation[3,1]),pr$rotation[3,2]*sign(pr$rotation[3,2]),pr$rotation[3,3]*sign(pr$rotation[3,3]),alpha=0.5,col=3,bbox=F) decorate3d() It seems fine; any advice is welcome best paolo Da: R-sig-ecology <r-sig-ecology-boun...@r-project.org> per conto di Paolo Piras <paolo.pi...@uniroma3.it> Inviato: martedì 19 gennaio 2016 14.46 A: r-sig-ecology@r-project.org Oggetto: [R-sig-eco] 3d fitting plane Hi folks, I look for a fast way to estimate a 3d fitting plane to my 3d data. I do not want z~y+x as this is a regression model. I just want the equation of the best plane that fits the data in 3d. Maybe using princomp() and Total least squares? Looking around I found some solutions but nothing definitive. Thanks in advance for any suggestion. best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] 3d box plot
Thanks to all who answered to my question; I found your comments very useful;
another option could be
http://www.cpwardell.com/2015/08/28/legoplots-in-r-3d-barplots-in-r/
My original purpose was to plot boxplots by groups for more than a variables
(as the comment below does) at different times (the third dimension)
best
paolo
Da: Jeremy Chacon <chaco...@umn.edu>
Inviato: luned� 9 novembre 2015 16.21
A: Paolo Piras
Cc: R-SIG list; r-sig-ecology@r-project.org
Oggetto: Re: [R-sig-eco] 3d box plot
This doesn't directly answer your question, but I would recommend just using a
2d boxplot grouped by the variables. I personally find the 3d one very
difficult to read. What about something like this, using ggplot2?
library(ggplot2)
fakeData = c(rnorm(10,1,2), rnorm(10,2,2),rnorm(10,2,2),rnorm(10,3,2))
group1 = c(rep("A",20),rep("B",20))
group2 = rep(c("X","Y"), 20)
df = data.frame(group1,group2, fakeData)
ggplot(df, aes(x = group1, y = fakeData, color = group2))+
geom_boxplot()
On Sun, Nov 8, 2015 at 5:11 AM, Paolo Piras
<paolo.pi...@uniroma3.it<mailto:paolo.pi...@uniroma3.it>> wrote:
Hi folks,
anyone could address me towards a R function/package able to do a 3d boxplot
similar to
http://158.132.155.107/oess/POSH/StatSoft/popups/popup125.gif
Thanks in advance
best
paolo
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University of Minnesota
Ecology, Evolution and Behavior
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[R-sig-eco] 3d box plot
Hi folks, anyone could address me towards a R function/package able to do a 3d boxplot similar to http://158.132.155.107/oess/POSH/StatSoft/popups/popup125.gif Thanks in advance best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] correlation between dissimilarity matrices
Hi Carlos Very briefly... you could look at rda() in vegan (not that in calibrate) partial and constrianed rda seems to be appropriate for your case. Maybe you should code a bit for controlling your nested stratification variable even if it is not very clear to me the nature of the data (sites vs location) I mean.. how exactly they are structured? best paolo Da: r-sig-ecology-boun...@r-project.org r-sig-ecology-boun...@r-project.org per conto di Carlos liuc...@hotmail.com Inviato: mercoledì 21 maggio 2014 20.57 A: r-sig-ecology@r-project.org Oggetto: [R-sig-eco] correlation between dissimilarity matrices Hello, I am working with two dissimilarity matrices containing dissimilarities in taxonomic community structure and environmental variables, respectively, between different sites. Those sites are grouped in three different locations. I know I can obtain the correlation between both matices by using a Mantel test. However, I would also like to know the correlation between both matrices after removing the effect of the dissimilarities between locations, i.e. only considering the dissimilarties within locations. How could I do it? Thank you very much, Carlos -- View this message in context: http://r-sig-ecology.471788.n2.nabble.com/correlation-between-dissimilarity-matrices-tp7578910.html Sent from the r-sig-ecology mailing list archive at Nabble.com. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Comparing results of two CCAs
Hi, maybe partial least squares: you can run two separate partial least squares analyses and then comparing vectors. best paolo Da: r-sig-ecology-boun...@r-project.org r-sig-ecology-boun...@r-project.org per conto di Eliot Miller eliotmil...@umsl.edu Inviato: venerdì 14 marzo 2014 05.50 A: r-sig-ecology@r-project.org Oggetto: [R-sig-eco] Comparing results of two CCAs I have four datasets: morphological measurements for a set of species (M1), ecological measurements for the same set of species (E1), morphological measurements for a second set of species (M2), and ecological measurements for this second set of species (E2). I am interested in finding the linear combinations of variables between M1 and E1, and between M2 and E2. That is, I'd like to know what combinations of morphological measurements are associated with what combination of ecological measurements--for each set of species separately. This seems like a good use of CCA (two separate CCAs). But here's where things get tricky for me. I'd like to see whether the same linear combinations from one set of species do a good job of explaining the variation in the second set of matrices. And I'd like to see how they differ, if possible...e.g. yes the canonical function from the first CCA does explain some of the variation in the second, but a different function could do a lot better. Is this making any sense? I could see simply running these as two separate CCAs, then comparing the results qualitatively. But that doesn't seem very rigorous. Should I be considering some other approach entirely? Thanks for any input! [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Comparing results of two CCAs
PLS can be performed in pls package while varpart in vegan package however...could you explain a little bit better the specific hypothesis you want to test? Different methods are suited in dependence of the explicit hypothesis you set. Da: eliot.is...@gmail.com eliot.is...@gmail.com per conto di Eliot Miller eliotmil...@umsl.edu Inviato: venerd? 14 marzo 2014 15.19 A: Paolo Piras; highs...@highstat.com Cc: r-sig-ecology@r-project.org Oggetto: Re: [R-sig-eco] Comparing results of two CCAs The partial least squares sounds really promising, thanks. I now need to go read about and try some tests with it. If you or anyone else has a preferred implementation of this in R I'd be interested in hearing about it! Alain--can you elaborate on how I might be able to use variance partitioning? I haven't used either of these methods before, but reading about it it sounds intended to quantify the amounts of variation in a single matrix explained by multiple matrices. I'm probably missing something. If you could explain more I'd be very interested. Thanks for your help! Eliot On Fri, Mar 14, 2014 at 2:16 AM, Paolo Piras paolo.pi...@uniroma3.itmailto:paolo.pi...@uniroma3.it wrote: Hi, maybe partial least squares: you can run two separate partial least squares analyses and then comparing vectors. best paolo Da: r-sig-ecology-boun...@r-project.orgmailto:r-sig-ecology-boun...@r-project.org r-sig-ecology-boun...@r-project.orgmailto:r-sig-ecology-boun...@r-project.org per conto di Eliot Miller eliotmil...@umsl.edumailto:eliotmil...@umsl.edu Inviato: venerd? 14 marzo 2014 05.50 A: r-sig-ecology@r-project.orgmailto:r-sig-ecology@r-project.org Oggetto: [R-sig-eco] Comparing results of two CCAs I have four datasets: morphological measurements for a set of species (M1), ecological measurements for the same set of species (E1), morphological measurements for a second set of species (M2), and ecological measurements for this second set of species (E2). I am interested in finding the linear combinations of variables between M1 and E1, and between M2 and E2. That is, I'd like to know what combinations of morphological measurements are associated with what combination of ecological measurements--for each set of species separately. This seems like a good use of CCA (two separate CCAs). But here's where things get tricky for me. I'd like to see whether the same linear combinations from one set of species do a good job of explaining the variation in the second set of matrices. And I'd like to see how they differ, if possible...e.g. yes the canonical function from the first CCA does explain some of the variation in the second, but a different function could do a lot better. Is this making any sense? I could see simply running these as two separate CCAs, then comparing the results qualitatively. But that doesn't seem very rigorous. Should I be considering some other approach entirely? Thanks for any input! [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.orgmailto:R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] envfit() in vegan
Thankyou very much Jari, actually this clarifies anything in my mind about this topic. What I was looking for is plotting the correlation vectors of my environmental variables (that in my hypothesis are the independent variables) on to my ordination scores of my dependent table. Graphically, envfit returns the plot I want but the orientation of these vectors are build under the opposite hypothesis that environmental variables are the dependent table. Maybe a manual solution for my need is to build a list of centered vectors each of which orientated upon the correlation between environmental variables and the ordination scores best paolo Da: Jari Oksanen jari.oksa...@oulu.fi Inviato: giovedì 17 ottobre 2013 9.32 A: Paolo Piras Cc: r-sig-ecology@r-project.org Oggetto: Re: [R-sig-eco] envfit() in vegan On 17/10/2013, at 02:49 AM, Paolo Piras wrote: Dear list, I write you because I do not understand the behavior of envfit() in vegan. Basically, it takes a matrix coming from an ordination procedure and it fits on it another matrix (often an environmental matrix). The projections of points onto vectors have maximum correlation with corresponding environmental variables. A permutation test is associated to this procedure and it basically performs a series of correlations between any column in the environmental matrix and the ordnation matrix. Maybe my question is trivial (or simply ...wrong) but..intuitively, this should return the same results found from a series of separate multivariate regressions between any single column in the environmental matrix and the entire ordination matrix. However it is not the case, being the envfit() results much more liberal when compared to regression (i.e. using rda) and the r2 are drastically larger than R-sq from rda. I suspect that the metric undergoing the permutation test of significance in envfit() that is squared correlation coefficient (r^2) does not correspond to the R-sq calculated using rda. Paolo, I am afraid I cannot quite understand your problems. A reproducible example with some numbers could be useful. I did not quite catch your comparison of RDA and envfit. They are quite different methods, and their R2's really are for different things (but with special tricks these things can be made similar). In RDA, the R2 tells how well the ordination predicts the species abundances, and in envfit() the R2 tells how well the ordination predicts the environmental variables. For a basic and normal usage of RDA let us compare the following cases: library(vegan) data(varespec, varechem) mod - rda(varespec ~ Al + P + K, varechem) ## gives unadjusted R2=0.377 envfit(mod ~ Al + P + K, varechem, display=lc, choices=1:3) ## **should** give for all vars r2=1 because they were the constraints Cheers, Jari Oksanen ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] envfit() in vegan
Dear list, I write you because I do not understand the behavior of envfit() in vegan. Basically, it takes a matrix coming from an ordination procedure and it fits on it another matrix (often an environmental matrix). The projections of points onto vectors have maximum correlation with corresponding environmental variables. A permutation test is associated to this procedure and it basically performs a series of correlations between any column in the environmental matrix and the ordnation matrix. Maybe my question is trivial (or simply ...wrong) but..intuitively, this should return the same results found from a series of separate multivariate regressions between any single column in the environmental matrix and the entire ordination matrix. However it is not the case, being the envfit() results much more liberal when compared to regression (i.e. using rda) and the r2 are drastically larger than R-sq from rda. I suspect that the metric undergoing the permutation test of significance in envfit() that is squared correlation coefficient (r^2) does not correspond to the R-sq calculated using rda. Thanks in advance for any advice best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] vegan RsquareAdj() for lm models
Dear list, I would like to easily compute the adjusted R-square in a lm model without intercept (excluding the intercept is essential for my analysis) I found that RsquareAdj() in vegan returns NA if the argument is a multiple-multivariate lm model thus including multivariate responses and multiple predictors, while it works for univariate response and multiple predictors. For example: library(vegan) yy-matrix(rnorm(200,0,1),ncol=4) xx-matrix(rnorm(200,0,1),ncol=4) RsquareAdj(lm(yy~xx-1)) RsquareAdj(lm(yy[,1]~xx-1)) There some specific reason for this behavior? Thanks in advance for any advice best regards Poalo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] vegan RsquareAdj() for lm models
Thankyou very much Jari! I think that it is nearly ok what I would like to have is the same as in RsquareAdj(vegan::rda(yy,xx)) that is a GLOBAL measure of the association BUT...I want it for a multiple-multivariate lm model that does not include the intercept; an alternative could be to build a rda design for the exclusion of intercept but I really cannot figure out how to do it. I think I just need to compute the average of single adjusted r squared from the output of your line of code, But the results are not identical EXAMPLE WITH INTERCEPT IN ORDER TO COMPARE WITH RDA RsquareAdj(vegan::rda(yy,xx)) mean(sapply(summary(lm(yy~xx)), function(x) c(r.squared = x$r.squared, adj.r.squared = x$adj.r.squared))[2,]) Or I just miss something in this computation Thanks again for any further suggestion Da: Jari Oksanen jari.oksa...@oulu.fi Inviato: giovedì 3 ottobre 2013 14.25 A: Paolo Piras; r-sig-ecology@r-project.org Oggetto: RE: [R-sig-eco] vegan RsquareAdj() for lm models Specific reason is that nobody has implemented this. These things don't come by automatic writing, but somebody must do them. What would you expect to get? Is this what was on your mind: sapply(summary(lm(yy~xx-1)), function(x) c(r.squared = x$r.squared, adj.r.squared = x$adj.r.squared)) Response Y1 Response Y2 Response Y3 Response Y4 r.squared 0.06845032 0.04788037 0.01702738 0.11253059 adj.r.squared -0.01255400 -0.03491264 -0.06844849 0.03535934 This could be implemented, but (1) is this what you or anybody else would like to have?, (2) how many things would it break by returning several values instead of one? If you want to have this, you really do not need to use vegan. vegan:::RsquareAdj.lm() takes its results from summary(lm_object). You can use that stats:::summary.lm directly. Cheers, Jari Oksanen From: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] on behalf of Paolo Piras [paolo.pi...@uniroma3.it] Sent: 03 October 2013 14:59 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] vegan RsquareAdj() for lm models Dear list, I would like to easily compute the adjusted R-square in a lm model without intercept (excluding the intercept is essential for my analysis) I found that RsquareAdj() in vegan returns NA if the argument is a multiple-multivariate lm model thus including multivariate responses and multiple predictors, while it works for univariate response and multiple predictors. For example: library(vegan) yy-matrix(rnorm(200,0,1),ncol=4) xx-matrix(rnorm(200,0,1),ncol=4) RsquareAdj(lm(yy~xx-1)) RsquareAdj(lm(yy[,1]~xx-1)) There some specific reason for this behavior? Thanks in advance for any advice best regards Poalo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] vegan RsquareAdj() for lm models
Thanks Jari, I understand Before going trough the code of rda I would prefer to see if I can do this using RsquareAdj When you say The default method can be called as RsquareAdj(x, n, m), and in the default method x is the unadjusted correlation...etc.. my problem is to extract the global unadjusted correlation that is a single global R-square; As I said, just averaging the single adjusted R-squared does not return the same values of RsquareAdj best paolo Da: Jari Oksanen jari.oksa...@oulu.fi Inviato: giovedì 3 ottobre 2013 15.56 A: Paolo Piras; r-sig-ecology@r-project.org Oggetto: RE: [R-sig-eco] vegan RsquareAdj() for lm models Paolo, See ?RsquareAdj for the call interface. The default method can be called as RsquareAdj(x, n, m), and in the default method x is the unadjusted correlation, n is the number of observations and m is the number of parameters (degrees of freedom) in the fitted model. Specific methods for univariate lm or for rda (and some others) find these variables in the result object, but then they just call the default method with the found x, n and m. You can build your model on that. It is possible to build a specific function for mlm objects, but nobody has done so in vegan. You cannot build an rda design without an intercept. It was a conscious design decision to make this impossible without hacking the rda.default code (I even say this in decision-vegan vignette). I am not going to make easy to have non-centred RDA: I care too much about people and i don't want to do evil. If you really *know* that you need non-centred RDA. then you know how to change those lines of code in rda.default. Cheers, Jari Oksanen From: Paolo Piras [paolo.pi...@uniroma3.it] Sent: 03 October 2013 15:52 To: Jari Oksanen; r-sig-ecology@r-project.org Subject: RE: [R-sig-eco] vegan RsquareAdj() for lm models Thankyou very much Jari! I think that it is nearly ok what I would like to have is the same as in RsquareAdj(vegan::rda(yy,xx)) that is a GLOBAL measure of the association BUT...I want it for a multiple-multivariate lm model that does not include the intercept; an alternative could be to build a rda design for the exclusion of intercept but I really cannot figure out how to do it. I think I just need to compute the average of single adjusted r squared from the output of your line of code, But the results are not identical EXAMPLE WITH INTERCEPT IN ORDER TO COMPARE WITH RDA RsquareAdj(vegan::rda(yy,xx)) mean(sapply(summary(lm(yy~xx)), function(x) c(r.squared = x$r.squared, adj.r.squared = x$adj.r.squared))[2,]) Or I just miss something in this computation Thanks again for any further suggestion Da: Jari Oksanen jari.oksa...@oulu.fi Inviato: giovedì 3 ottobre 2013 14.25 A: Paolo Piras; r-sig-ecology@r-project.org Oggetto: RE: [R-sig-eco] vegan RsquareAdj() for lm models Specific reason is that nobody has implemented this. These things don't come by automatic writing, but somebody must do them. What would you expect to get? Is this what was on your mind: sapply(summary(lm(yy~xx-1)), function(x) c(r.squared = x$r.squared, adj.r.squared = x$adj.r.squared)) Response Y1 Response Y2 Response Y3 Response Y4 r.squared 0.06845032 0.04788037 0.01702738 0.11253059 adj.r.squared -0.01255400 -0.03491264 -0.06844849 0.03535934 This could be implemented, but (1) is this what you or anybody else would like to have?, (2) how many things would it break by returning several values instead of one? If you want to have this, you really do not need to use vegan. vegan:::RsquareAdj.lm() takes its results from summary(lm_object). You can use that stats:::summary.lm directly. Cheers, Jari Oksanen From: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] on behalf of Paolo Piras [paolo.pi...@uniroma3.it] Sent: 03 October 2013 14:59 To: r-sig-ecology@r-project.org Subject: [R-sig-eco] vegan RsquareAdj() for lm models Dear list, I would like to easily compute the adjusted R-square in a lm model without intercept (excluding the intercept is essential for my analysis) I found that RsquareAdj() in vegan returns NA if the argument is a multiple-multivariate lm model thus including multivariate responses and multiple predictors, while it works for univariate response and multiple predictors. For example: library(vegan) yy-matrix(rnorm(200,0,1),ncol=4) xx-matrix(rnorm(200,0,1),ncol=4) RsquareAdj(lm(yy~xx-1)) RsquareAdj(lm(yy[,1]~xx-1)) There some specific reason for this behavior? Thanks in advance for any advice best regards Poalo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R
Re: [R-sig-eco] simple question about nmds
Hi, actually I think you CAN use them (as predictors or response either) because, albeit in its unique way, NMDS **IS** a method to summarize a highly multidimensional phenomenon. best paolo Da: r-sig-ecology-boun...@r-project.org [r-sig-ecology-boun...@r-project.org] per conto di Simone Ruzza [simone.ruzz...@gmail.com] Inviato: lunedì 10 giugno 2013 10.26 A: r-sig-ecology@r-project.org Oggetto: Re: [R-sig-eco] simple question about nmds Apolologies, I re-phrase what I have said before: I would be interested using the site scores within a multiple regression settings, but the the scores a response rather than predictors. Best wishes, Simone On Mon, Jun 10, 2013 at 9:19 AM, Simone Ruzza simone.ruzz...@gmail.comwrote: Dear list, apologies for the total beginner's question. I was wondering whether one can use the site scores of an NMDS ordination to do do further analyses as typically done for other ordination methods, e.g. use the axis scores as predictors in a multiple regression settings. I think it should not be possible, because the aim of NMDS is not to summarize the major patterns of variations withing a multivariate dataset. Do you confirm? thanks in advance, Simone [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] multivariate continuous response and ordinal predictor(s)
Dear all, I need to run a model with multivariate continuous responses and one (or more) ORDINAL (i.e. 1,2,3, etc.) predictor variables; these are not factors because are ordinal; the more intuitive solution could be to apply a standard lm() but I ask you if some more appropriate strategies can be adopted. I saw VGAM package but it does not seem to do what I want Thankyou in advance for any advice best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] simple quetion about posthoc in adonis
I saw in some forum some considerations about this topic. Maybe I propose a brutal (perhaps incorrect?) solution; given more than two groups in a model subjected to permutation in adonis.and if the global model is significant if one contrasts pairwise all possible pair comparisons between groups in a subset containing just those two groups one could use that as a posthoc? Of course some p-value correction can be applied ex-post using p.adjust(). thankyou in advance for any advice best paolo ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
