similar to: Oja median

Hi, Can we get the code for calculating Oja median for multivariate data Thanks and Regards Rahul Agarwal Analyst Equities Quantitative Research UBS_ISC, Hyderabad On Net: 19 533 6363 [[alternative HTML version deleted]]

Hi Roger, As we know that The Oja median has (finite) breakdown point 2/n, i.e., is not robust in any reasonable sense, and is quite expensive to compute, so do we have some better methodology to compute multivariate median Rahul Agarwal Analyst Equities Quantitative Research UBS_ISC, Hyderabad On Net: 19 533 6363 [[alternative HTML version deleted]]

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Dear R experts, we are preparing an R-package to compute the Oja Median which contains some C++ code in which random numbers are needed. To generate the random numbers we use the following Mersenne-Twister implementation: // MersenneTwister.h // Mersenne Twister random number generator -- a C++ class MTRand // Based on code by Makoto Matsumoto, Takuji Nishimura, and Shawn Cokus // Richard J.

Hello, I am hoping someone can help me with the following multivariate issue: I have a model consisting of about 50 covariates. I would like to reduce this to about 5 covariate for the reduced model by combining cofactors that are strongly correlated. Is there a package or function that would help me with this in R? I appreciate any suggestions. Thanks, Ian

Hi ALL, I have very simple question regarding pattern matching. Could anyone tell me how to I can use R to retrieve string pattern from text file. for example my file contain following information SpeciesCommon=(Human);SpeciesScientific=(Homo sapiens);ReactiveCentres=(N,C,C,C,+ H,O,C,C,C,C,O,H);BondInvolved=(C-H);EzCatDBID=(S00343);BondFormed=(O-H,O-H);Bond+

I have following codes for ggplots. The legends are given in the plot do not match with the values specified in the codes given below. Your helps highly appreciated. Greg library(ggplot2) p <- ggplot(a,aes(x=NO_BMI_FI_beta ,y=FI_beta ,color= Super.Pathway))+ theme_bw() +theme(panel.border=element_blank()) + geom_point(size=3) p2<-p+scale_color_manual(name="Super.Pathway",

I use the step() function occasionally, and I think I understand its objective, proper use, and limitations. Now I see stepwise model selection being used in what seems to be an unusual way, and I wonder if it is right or wrong. May I describe? Genetic mapping tries to find where in an animal's genome are genetic elements that influence a particular physical trait. Say there are 100

I have 8 cofactors possibly affecting one and only one variable. I make conditional histograms: <-pdf(file="tst3.pdf",paper="special",width=36,height=36) <-histogram(~Oversized|dat$c1*dat$c2*dat$c5*dat$c6*dat$c7*dat$c8*dat$c9*dat$c10,nint=21,layout=c(32,8),data=dat,type="count") <-dev.off() This works (compliments to R developers!) but it does generate a

Dear all, ? I am implementing a stochastic utility model that will eventually make use of multinomial logit. I found that there is a package in R called mlogit. I am not sure whether I have already found the correct package or software. May I ask am I correct? ? Basically, let's say ? I have observations of n outcomes, for each outcome 1<=i<=n, they were selected by a choice from a set

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Dear R experts, I'm planning to write some kind of multivariate regression function where I would like to use a formula method. My question is if there is anywhere some detailed introduction how to program formulas in R? A bit more about what I need: I would like to start implementing a multivariate hierarchical regression model and to keep it simple in the beginning I would restrict

Hello Xen-Users, I am currently provisioning old Linux guests as HVM on a server, and giving clients access through VNC. The current setup uses the default Xen framebuffer as a VNC server (all configuration files have vnc=1 and a few other settings), and my clients are using vinagre (a.k.a. Remote Desktop Viewer) on Arch as their VNC client. Problem: Keyboard input is not consistently handled

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

Hi All, (I have a degree in math, but I am too embarassed to ask my colleagues, so here goes:) I would like to get a vector normal (orthogonal) to a plane formed by two other vectors. In matlab I do this: v1 = [.4, .6, .8]; v2 = [.9, .7, .2]; nn = cross(v1,v2) (gives ~[-.48, .65, -.24] if I do R> cross(v1, v2), I get .94. Huh? Thanks for all your help, again. W

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

Dear help community, I'm a R-beginner and use it for my master thesis. I've got a mixed model and want to analyse it with lme. There are a lot Cofactors that coult be relevant. To extract the important ones I want to do the stepAIC, but always get an error warning. Structure of my data: data.frame': 72 obs. of 54 variables: $ Block : Factor w/ 3 levels

Hi! I came across R just a few days ago since I was looking for a toolbox for cox-regression. I?ve read "Cox Proportional-Hazards Regression for Survival Data Appendix to An R and S-PLUS Companion to Applied Regression" from John Fox. As described therein plotting survival-functions works well (plot(survfit(model))). But I?d like to do some manipulation with the survival-functions

Greetings, I have the following? Problem: Given k (=10) discrete independent random variables X_i with n_i (= 5 to 20) values each,compute quantiles of the distribution of the sum X = X_1+...+X_k. Here X has n=n_1 x n_2 ... n_k distinct values which is too large to list them all together with their probabilities. I tried several approaches: (A) Convolution: each X_j is approximated with