similar to: polynomial svms and in-sample error

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,

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

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

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

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

I'm making tables for prediction results of classifiers (2 classes) that show the usual numbers, true positives, false positives, etc I used the command table(predictedLabels,realLabels) to make those. I just had a case though ,where one of the label vectors had only one class in it. This will result in only half a table. Compare: x<-c(1,1,1,0,0) y<-c(1,1,1,0,1) table(x,y) to

I'm trying to find a way to fit a polynomial of degree n in x and y to a set of x, y, and z data that I have and obtain the coefficients for the terms of the fitted polynomial. However, when I try to use the surf.ls function I'm getting odd results. > x <- seq(0, 10, length=50) > y <- x > f <- function (x, y) {x^2 + y} > library(spatial) > test <-

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

When doing polynomial regression I believe it is a good idea to use the poly function to generate orthogonal polynomials. When doing this in Splus there is a handy function (transform.poly I think) to convert the coefficients produced by regression with the poly function back to the original scale. Has somebody written something similar for R ? Robert

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

Full_Name: Tamas K Papp Version: 2.4.0 OS: linux Submission from: (NULL) (140.180.166.160) I was using the lines.polynomial method for plotting piecewise polynomials (parts of splines). I needed a feature to limit the range of plotting using a parameter given to the function (as opposed to par("usr")). I think that the following changes would be a nice addition: lines.polynomial

Are there implementations of, e.g. Neville's algorithm, for interpolating polynomials through some data points? Nevilles' is an improvement on Lagrange interpolation. And how about interpolating rational functions? I could not find anything at rseek.org or at crantastic.org. thanks -- View this message in context:

What does the warning message "1: Singular precision matrix in level -1, block 1" mean? I get this warning 50+ times when I try to fit the following model lme( response ~ covariateA + poly(covariateB,3), ~poly(covariateB,3)|group ) It's not a small dataset - a set of up to 20 blood pressure readings on just over 2000 people, and I don't get the error message when I try to fit

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal