similar to: ordiellipse

Hi Listers, This has a simple answer but it has been eluding me nonetheless. I have been building a PCA plot from scratch with the ability to plot predefined groups in different colors. This has worked fine but when I try to get a polygon drawn around each of the groups it is not recognising my colour file correctly and is only printing the first colour in the file....code is below

Hello R experts, I am trying to plot ordiellipse for my data but the col according to factors. Metabolites_raw= read.csv(file.choose(), head = TRUE) #file 21Metabolites.csv Metabolites_t=t(Metabolites_raw[,2:82]) ord <- metaMDS(Metabolites_t, distance="bray") symbol=as.numeric(Metab_metadata$LandType) col.list <-

Hello, R-Help - I am trying to plot the results of an ordination from package vegan. The tricky part for me right now is getting the colors of the ellipses denoting the 95% confidence intervals of the group centroids to match the colors of the points for those same groups. >From and earlier post, I saw the code to make a plot of the ordination using different colors for my different groups.

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Dear R-core/development-team, The problem noted in the subject-line has been a problem in the last three development versions of R for Windows that I have downloaded and tested, the most recent of them being a version I downloaded this morning. Update.packages() using the graphical option, i.e. called as update.packages(ask='graphics', checkBuilt=TRUE) does not work as it should, but

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Hi Listers, Is it possible to produce an ordination plot in 2d, where bubbles represent the location of sites (this part is easy enough) and the size of the bubbles is proportional to the sites location in 3d space (I am stuck on this option). So sites that are very near the 2d plane of the xy axes would be larger while sites that are actually further away in 3 d space would be proportionally

Hi All, I have to say upfront that I am a complete neophyte when it comes to programming. Nevertheless I enjoy the challenge of using R because of its incredible statistical resources. My problem is this .........I am running a regression tree analysis using "rpart" and I need to run the calculation repeatedly (say n=50 times) to obtain a distribution of results from which I will pick

Hi All, I have a dataset consisting of abundance counts of a fish and I want to test if my data are poisson in distribution or normal. My first question is whether it is more appropriate to model my data according to a poisson distribution (if my test says it conforms) or use transformed data to normalise the data distribution? I have been using the vcd package gf<-goodfit(Y,type=

Dear R-users, I am a newbie to this site and a relative new-comer to S/R, so please tread lightly, for you tread... There have been several posting relating to problems with augPred() from the nlme library. Here is a "fix" for one of these problems which may lie at the root of others. In my case the problem with augPred() lay in gsummary(), which augPred() uses, causing it to fail.

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)),

Hi Listers, I am trying to reflect a PCA biplot in the x-axis (i.e. PC1) but am not having much success. In theory I believe all I need to do is multiply the site and species scores for the PC1 by -1, which would effectively flip the biplot. I am creating a blank plot using the plot command and accessing the results from a call to rda. I then use the calls to scores to obtain separate site and

I have used the fourthcorner function as suggest by dray and legendre (model 2 and 4 then combine). I plot the combined value with plot(four.comb, type="G"). What do the colors mean? I have both grey and black bars. many thanks, Stephen -- Stephen Sefick ************************************************** Auburn University Biological Sciences 331 Funchess Hall Auburn, Alabama

I've read something about this problem, but I don't know how can i avoid this problem. Why the order of the factors give different results? I suppose it's because the order of the factors, i've just changed "lcc" from the first position to the last in the model, and the significance change completely >

Hello, I'm using the R-2.13.1 version in Windows and I'm trying to do a robust Pca with the following: x<-matrix(0.5,30,30) library("rrcov") y<-PcaCov(x) The following error occurs: Error: diff(sv)<0 ist not all TRUE The same error occurs with the other functions. What does this mean and how can I perform the robust PCA with these functions by using a quadratic

Hi, I have a problem with the ordered probit model -polr function (library MASS). My independent variables are countinuos. I am not able to understand two main points: a) how to calculate marginal effects b) how to calculate the relative importance of each independent variables If required i will attach my model output. Thanks Franco

Hi dear, I want to estimate d.f for Chi-squared distribution: est.chi[i,]<-c( fitdistr(as.numeric(data2[,i]),"chi-squared",start=list(df=1))$estimate)Warning message:In optim(x = c(7.86755, 7.50852, 7.86342, 7.70589, 7.70153, 7.58272, : one-diml optimization by Nelder-Mead is unreliable: use "Brent" or optimize() directly Who can help me to solve this problem? Best

Hi, I'm trying to graph some data in a boxplot-like style, but I want to set the box and whisker limits myself (rather than having R calculate them for me). I'd like the boxes to be shaded and the whiskers to be dotted lines. My data are set up is something like this: min.whisker max.whisker min.box max.box species 0 200 20 37