similar to: Centering (e.g. on prcomp)

Hi! I am getting a lot of numbers in the background of the pca screeplots if i use call("plot") and eval(somecall). Til now, creating the calls and plotting later on this way worked fine. Example: pcaI<-prcomp(iris[,1:4]) plot(pcaI) x<-call("plot",pcaI) eval(x) Anyone got an idea how i can avoid that? (also it might take a second or so for the numbers to appear,

with pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs] dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2))); results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc", C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T) and a degree of 5 i get an error of 0 reported by the ksvm

I do not understand, from a PCA point of view, the option center=F of prcomp() According to the help page, the calculation in prcomp() "is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using eigen on the covariance matrix" (as it's done by princomp()) . "This is generally the preferred method for numerical accuracy"

Might not be the best place to ask, but i could get lucky.. I have setup an eclipse environment to write sweave files lately and wanted to switch to knitr. I could get it to work on easy files, but my earlier written sweave file fails to be knit properly. Here is the error message: Quitting from lines 273-276: Error in setwd(base.dir) : kann Arbeitsverzeichnis nicht wechseln (it says: cannot

Hi everyone I have banking data set in long format with 4 columns.One of these columns is bank name which consist of 49 banks and I want the data for only 40 banks out of these 49 so can anyone help me on how to get this 40 banks data My data looks like Year Name totalliabilties assets 1990 a 90 10 1991 a 89 48 1992 a 87

Some webpage has described prcomp and princomp, but I am still not quite sure what the major difference between them is. Can they be used interchangeably? In help, it says 'princomp' only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as

When I look through archives at https://stat.ethz.ch/pipermail/r-help/2003-October/040525.html I see this: Liaw, Andy wrote: >In the `Detail' section of ?princomp: > >princomp only handles so-called Q-mode PCA, that is feature extraction of >variables. If a data matrix is supplied (possibly via a formula) it is >required that there are at least as many units as variables. For

Following problem: Say you have a bunch of parameters and want to produce results for all combinations of those: height<-c("high","low") width<-c("slim","wide") then what i used to do was something like this: l<-list() for(h in height){ l[[h]]<-list() for(w in width){ l[[h]][[w]] <- doSomething() } } Now those parameters aren't

Hello R-Help! I've looked around and have not found: A simple(short) way to hide functions and variables from the global environment. What i want is for a few of them to only be accessable from the scriptfile they're in. I probably could do fun things with environments , but that seems quite a hassle. As example: I have a file that gets me stuff from the database and creates an R object

Can i somehow append objects to an .Rdata file? I didn't see an option for it in the save() method. dump() won't work since i have s4 objects in there.

From: ?????? <NAVER-MAILER at naver.com> Subject: [???? ??] envy721c at naver.cobSDsnLzroZwg66mU7J287J20IOyghOyGoeuQmOyngCDrqrvtlojsig==teuLiOuLpC4= The only plain english in the message is that the mail was denied by the receiver Anyone else getting this?

Hi! If i found a problem with the code of one of the libraries (not core), or, in my current case, would wish something minor changed for convenience, where can i get contact? Can i put it in the "official" bug repository? (Problem discription for anyone interested: Why call the default function kpca for a matrix with kpar=list(sigma=0.2), instead of putting this default sigma into the

Dear all, perhaps I am using princomp.formula and prcomp.formula in a way that is not documented to work, but then the documentation just says: formula: a formula with no response variable. Thus, to avoid a lot of typing, it would be nice if one could use '.' and '-' in the formula, e.g. > library(DAAG) > res <- prcomp(~ . - case - site - Pop - sex, possum)

Dear friends, I am beginning to use R software in my academic research and I'm having some problems regarding the use of PCA. I have a table with 24445 rows and 9 columns, and I used the function prcomp() to do the analysis. Working with an example?: x<-read.table("test.txt", header=T) row.names(x)<-x[,1] x<-x[,-1] require(stats) pca<-prcomp(x, scale=T) names(pca) ##

I have a decent sized matrix (36 x 11,000) that I have preformed a PCA on with prcomp(), but due to the large number of variables I can't plot the result with biplot(). How else can I plot the PCA output? I tried posting this before, but got no responses so I'm trying again. Surely this is a common problem, but I can't find a solution with google? The University of Dundee is a

Hello all, I wish to compute site scores using PCA (prcomp) on a matrix with missing values, for example: Drain Slope OrgL a 4 1 NA b 2.5 39 6 c 6 8 45 d 3 9 12 e 3 16 4 ... Where a,b... are sites. The command > pca<-prcomp(~ Drain + Slope + OrgL, data = t, center = TRUE, scale = TRUE, na.action=na.exclude) works great, and from

Hi All, I need to normalize/scale string variable which represents interests of customers (e.g., 'cycling, rollerblading, swimming' etc). Does anybody know how to do this, I want then use it along with other numeric variables for SVM classification. Appreciate for any advice. -Alex [[alternative HTML version deleted]]

I'm not sure what the parameter specifies: random if an integer value is specified, random parameter vectors are drawn from the parameter space. What are the parameter vectors and what is the parameter space? What means drawn? greetings Jessi [[alternative HTML version deleted]]

Hi! how do i get to the source code of kpca or even better predict.kpca(which it tells me doesn't exist but should) ? (And if anyone has too much time: Now if i got that right, the @pcv attribute consists of the principal components, and for kpca, these are defined as projections of some random point x, which was transformed into the other feature space -> f(x), projected onto the actual

Hi R People: When performing PCA, should I use prcomp, princomp or fast.prcomp, please? thanks. Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com