similar to: ltsreg warnings (PR#1184)

I'm trying to use least trimmed squares using ltsreg with nsamp="exact". When I use the following: rg <- ltsreg(x,y,nsamp="exact") I get: Error in lqs.default(x, y, nsamp = "exact", method = "lts") : NAs in foreign function call (arg 10) In addition: Warning message: NAs introduced by coercion Incidentally, there are no missings in x or y,

I now have versions of code that is destined (I believe) for 0.63 which is in a suitable state for comment. The files are at ftp://ftp.stats.ox.ac.uk/pub/R (Our www server is being moved, so may be intermittently down, but this ftp server should be stable.) All are R packages, for the moment for personal use only (no re-distribution). Use with 0.62.3 or 0.63 (although I am aware of some

> 3x4 Error: unexpected symbol in "3x4" R has no idea that you equate "x" as multiplication.. use an astrix > 3*4 [1] 12 dominic wrote > > This is basically my code: > > library(MASS) > lmsreg(formula = b0 ~ b1 + b3 + b1xb2, data=mydata) > > b1xb2 is an interaction but it was the centered value for a continuous > variable times a

Hi, I am using robust regression, i.e. model.robust<-ltsreg(MXD~ORR,data=DATA). My question:- is there any way to determine the Robust Multiple R-Squared (as returned in the summary output in splus)? I found an equivalent model in the rrcov package which included R-square, residuals etc in it's list of components, but when I used this package the only results returned were equivalent to

Hello, My name is edwin, I come from Indonesia, Can you help me, I want package which is contain lqs and lmsreg. I need fast... Can you help me to create bootstrap regression ?? Thx Edwin [[alternative HTML version deleted]]

Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Does R have a function like the S(plus) function, lmsreg, Least Median of Squares Regression? I am using R-0.61. Thank you, Mike Fleming mfleming at nass.usda.gov -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

#I would like to use the below data to make a scatter plot matrix with #code similar to that below the data #conceptually this is the right approach I think #thanks in advance melt.gg <- structure(list(stream = c("Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower", "Bonham Lower",

Hello I want to ask if the estimator method LTS (Least Trimmed Squares) is implemented in R. I've found the lqs(y~x,method = c("lts")) tool that implements LTS but minimazing the sum of the `quantile' smallest squared residuals. I don't know if this is the same as the clasical LTS, if it is, where do I set the trim (h value to trim the LS sum)? I'll be waiting

Hi all, I have a problem with getting my code to do what I want! This is the code I have: create.means.one.size<-function(nsample,var,nboot){ mat.x<-matrix(0,nrow=nboot,ncol=nsample) for(i in 1:nboot){ mat.x[i,]<-sample(var,nsample,replace=T) } mean.mat<-rep(0,nboot) for(i in 1:nboot){ mean.mat[i]<-mean(mat.x[i,]) } sd.mean<-sd(mean.mat) return(mean.mat) } where

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

Dear List-Members, I have a problem with the function lqs() from package MASS. In some cases it produces different results for the same settings and needs a random seed to be set, in other cases not. I really cannot understand, why this happens. As well I do not understand what exactly you need the random seed for. Is it a starting point for iterations? Or do different results occur because of

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

Hi sri, I think that I see what you mean. Your statements: x = Count_A_less_than_max of (Count type B) y = Count_A_higher_than_max of (Count type B). I took to mean that you wanted a logical value for x and y. Looking more closely at your initial message, I see that you wanted _all_ values of A with respect to maxB in x and y. The error with maximum values was due to a typo. Perhaps this will do

If I have a dataframe which is organized like this: name color likes? 1 sally red 0 2 sally blue 1 3 sally green 1 4 jake red 0 5 jake blue 1 6 jake green 1 7 tom red 1 8 tom blue 0 9 tom green 0 And I want to create a matrix in the form: red blue green sally 0 1 1 jake 0 1 1 tom 1 0 0 Are there any built-in

People of R(th), I have been ramming my head against this problem, and I wondered if anyone could lend a hand. I want to parallelize a bootstrap of a linear model on my 8-core mac. Below is the process that I want to parallelize (namely, the m2.ph.rlm.boot<-boot(m2.ph,m2.ph.fun, R = nboot) command). This is an extension of the bootstrapping linear models example in Venables and Ripley to

I need to make nonlinear regression with the posterior script, but how is the problem? I have error in library (nls), package 'nls' has been merged into 'stats'. I need help? What other forms I have to make nonlinear regression? and how I find to calculate statistics y residuals, scatterplot. thanks SCRIPT ros<-read.table("Dataset.csv",header=T,sep=",")