search for: varing

If you submit these lines, you end up with variable "vare.dis". I want to export vare.dis to csv. Stuck I am. library(vegan,logical.return = TRUE) #return=true verifies package is available library(MASS,logical.return=TRUE) #return=true verifies package is available data(varespec) #varespec is an example data file in the vegan package vare.dis <- vegdist(varespec)

Hello This is a simple question but I couldn't google an answer. In the procrustes function of the vegan package, one uses plot(procrustes_object, kind=2) to obtain a plot of the residual differences. For instance: data(varespec) vare.dist <- vegdist(wisconsin(varespec)) library(MASS) mds.null <- isoMDS(vare.dist, tol=1e-7) mds.alt <- isoMDS(vare.dist,

Dear all, I realised a correspondence analysis with function cca() of vegan library. Just like in Okansen (2010) in the example of R help: library(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec~ Al + P + K, varechem) With plot.cca() function I represented the species matrix in the next way: plot(vare.cca,display="species") Being similar to: plot((c(-2,2)),(c(-2,2)),

I performed canonical correspondence analysis (cca) with the example data of vegan, but I'm not able to obtain a table like scores() for the constraining variables. I can see them in the summary() mode, but it would be great to have in a separate table. Any suggestion?, thanx Gianandrea require(vegan) data(varespec) data(varechem) vare.cca<-cca(varespec,varechem) scores(vare.cca)

Dear List, I am writing a custom panel function and xyplot method to plot the results of a procrustes analysis from the vegan package. I am having trouble getting the call to panel.arrows to work as I wish when conditioning. The attached file contains the function definitions for the xyplot method and the custom panel and prepanel functions I am using. This example, using data and functions from

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

Hi, I have a couple questions about fitting environmental (land use factors, plant species presence-absence, and soil variables) constraints to my CCA biplot. 1. After successfully plotting species and site scores in my CCA, I have been trying to insert the biplot arrows of the environmental constraints in my data set using the text() function. When I do that, the plot changes completely. Is there

Hello! I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). Right now I am looking for a piece of software to fit a 1D data sample to a mixture of t-distributions. I searched quite a while already and it seems to be that this is a somehwat obscure endeavor as most

I don?t see how neither a) or b) applies to this question nor the technical merit of the remark about mixture models. Do you have a suggestion for a more appropriate forum for this issue/question? (stackoverflow basically sent me here). Kind regards > On 29. Jun 2017, at 16:58, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Offlist, because this is (a) an opinion and (b)

Dear r-helpers, I just read in an article by Virtanen et al. (2006) where vegetation-environment relationships are studied by fitting smoothed surfaces on an NMDS ordination using GAMs (Wood 2000). The authors describe, that they used R? as goodness-of-fit statistic, which they compare to the R? of fitted vectors. Calculations were carried out using the package vegan (Oksanen). I know that I can

Dear R-list, I have a data set (in the following example called "a") which have: one "subject indicator" variable (called "id") three dependent variables (varD, varE, var F) three independent variables (varA, varB, varC) I want to fit 9 lme models, one per posible combination (DA, DB, DC, EA, EB, EC, FA, FB, FC). In stead of writting the 9 lme models, I want to

HI Guys, I know that this forum is not for homework but I am trying to interpret R output code. I was just wondering if someone might be able to help. I have been given the following. For (X1,X2) distributed bivariate normal with parameters mu1 = 5.8 mu2 = 5.3 sd1 = sd2 = 0.2 and p = 0.6 The r-code and inpit/output are as follows input m <- 5.3 + 0.6*(6.3 - 5.8) s <-

Add this: --delete-excluded See the manual. Rainer -----Urspr?ngliche Nachricht----- Von: rsync-bounces+rsync=diplan.de@lists.samba.org [mailto:rsync-bounces+rsync=diplan.de@lists.samba.org]Im Auftrag von Mandrake Pe Gesendet: Mittwoch, 29. Oktober 2003 13:33 An: rsync@lists.samba.org Betreff: trying to figure out how the --delete option works. I use this comand to back up my entirely drive

Would package "teigen" help? Ranjan On Thu, 29 Jun 2017 14:41:34 +0200 vare vare via R-help <r-help at r-project.org> wrote: > Hello! > > I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). > > Right now I am looking for a piece of

Offlist, because this is (a) an opinion and (b) about statistics and therefore offtopic. I don't know whether any such package exists, but I would predict that this is likely to be overdetermined (too many parameters) and therefore unlikely to be a successful strategy. Fitting a mixture of Gaussians is already difficult enough. Feel free to ignore, of course, and no need to reply. Cheers,

Hi All, I am using canonical correspondence analysis to compare a community composition matrix to a matrix of sample spatial relationships and environmental variables. In order to parse out how much variance is explained purely by space (S/E) or the environment (E/S) I am using a conditional (partial) CCA. I want to test significance via Monte Carlo but I can not find a way to do this with a

Hello, I am interested in using the capscale function of vegan package of R. I already have a dissimilarity matrix and I am intended to use it as 'distance' argument. But then, I don't know what kind of data must be in 'comm' argument. I don't understand what type of data must be referred as 'species scores' and 'community data frame' since my data refer to

Thanks Ranjan, that provides exactly what I need. I also got a more elaborate answer on stackoverflow (after the same question got rejected form cross validated?) with a running example for this package: https://stackoverflow.com/questions/44825529/package-to-fit-mixtures-of-student-t-distributions/44827220#44827220

Hi, I want to calculate the t- and p-values for a linear model using the Newey West estimator. I tried this Code and it usually worked just fine: > oberlm <- lm(DYH ~ BIP + Infl + EOil, data=HU_H) > coeftest(oberlm, NeweyWest(oberlm, lag=2)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.1509950 0.0743832 2.0300 0.179486 BIP

...fferent altitude. In gnuplot, the corresponding command 'splot' will generate a picture beside to indicate the relationship between color and altitude. But in R, how to draw it? I have read the manual of legend, but they are all about how to draw a legend with colored text, not a continuous varing color with corresponding number.