similar to: problem in loop

An embedded and charset-unspecified text was scrubbed... Name: not available URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20091126/7134fc17/attachment-0001.pl>

Dear all, I am a new user in r and I am facing some problems with the quantile regression specification. I have two matrix (mresultb and mresultx) with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10. Hence, the columns in my matrix represents each simulation of a determined variable. I need to regress each column of mresultb on mresultx. My codes are the following:

Dear all, I have just sent an email with my problem, but I think no one can see the red part, beacuse it is black. So, i am writing again the codes: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N <- 200 I <- 5 taus <- c(0.480:0.520) h <-

Running the function below, tested using the cardiff dataset from splancs generates the following error. What changes do I need to make to get the function to work? Thanks. --Dale > gen.rpoints(events, poly, 99) > rpoints Error: object "rpoints" not found # test spatial data library(splancs) data(cardiff) attach(cardiff) str(cardiff) events <- as.points(x,y) ###

Dear all, I am trying to run a loop in my codes, but the software returns an error: "subscript out of bounds" I dont understand exactly why this is happenning. My codes are the following: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Hi all I am analyzing a data set containing information about the behaviour of marine molluscs on a vertical wall. Since I have replicate observations on the same individuals I was thinking to use the geepack library. The data are organised in a dataframe with the following variables Date = date of sampling, Size = dimensions (mm) Activity duration of activity (min) Water = duration of

I?m working on an R-implementation of the simulation-based finite-sample null-distribution of (R)LR-Test in Mixed Models (i.e. testing for Var(RandomEffect)=0) derived by C. M. Crainiceanu and D. Ruppert. I'm in the beginning stages of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the

## Package Needed library(fields) ## Assumptions set.seed(123) nsim<-5 p<-2 ## Generate Random Matrix G G <- matrix(runif(p*nsim),nsim,p) ## Set Empty Matraces dmax and dmin dmax<- matrix(data=NA,nrow=nsim,ncol=p) dmin<- matrix(data=NA,nrow=nsim,ncol=p) ## Loop to Fill dmax and dmin for(i in 1:nsim) { dmax[i]<- max(rdist(G[i,,drop=FALSE],G)) dmin[i]<-

Hello, I have the following r-codes for solving a quasilikelihood estimating equation: >library(geepack) >fit<-geese(y~x1+x2+x3,jack=TRUE,id=id,scale.fix=TRUE,data=dat,mean.link = "logit", corstr="independence") Now my question is how can I calculate the confidence interval of the parameters of the above model "fit"? [[alternative HTML version deleted]]

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation

Hi all, I would like to estimate power and necessary sample size for a GLM with a response variable that has a poisson distribution. Do you have any suggestions for how I can do this in R? Thank you for your help. Sincerely, Craig -- Craig A. Faulhaber Department of Forest, Range, and Wildlife Sciences Utah State University 5230 Old Main Hill Logan, UT 84322 (435)797-3892

Dear all, Does anyone have any idea on how to speed up the for() loop below. Currently it takes approximately 2 minutes and 30 seconds. Because of the size of Nsim and N, simulating a multivariate normal (instead of simulating Nsim times a vector of N normal distributions) would require too much memory space. Many thanks for your kind help, Simona N=3000 PD=runif(N,0,1) cutoff.=qnorm(PD)

I normally use the following code to create a figure displaying the mark correlation function for the point pattern process "A": M<-markcorr(A) plot(M) I have now started to use the following code to perform 1000 Monte Carlo simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test based on envelopes of the Mark correlation function obtained from simulated point

I've written the function below to simulate the mean 1st through nth nearest neighbor distances for a random spatial pattern using the functions nndist() and runifpoint() from spatsat. It works, but runs relatively slowly - would appreciate suggestions on how to speed up this function. Thanks. --Dale library(spatstat) sim.nth.mdist <- function(nth,nsim) { D <- matrix(ncol=nth,

Craig, Thanks for your follow-up note on using the asypow package. My problem was not only constructing the "constraints" vector but, for my particular situation (Poisson regression, two groups, sample sizes of (1081,3180), I get very different results using asypow package compared to my other (home grown) approaches. library(asypow) pois.mean<-c(0.0065,0.0003) info.pois <-

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Hi R users: I'm trying to make a function where two of the parameters are functions, but I don't know how to put each set of parameters for each function. What am I missing? I try this code: f2<-function(n=2,nsim=100,fun1=rnorm,par1=list(),fun2=rnorm,par2=list()){ force(fun1) force(fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2)

Hi everyone, I'm working on a script trying to use foreach %dopar% but without success, so I manage to run the code with foreach %do% and looks like this: The code is part of a MCMC model for projects valuation, returning the most important results (VPN, TIR, EVA, etc.) of the simulation. foreach (simx = NsimT, .combine=cbind, .inorder=FALSE, .verbose=TRUE) %do% { MCPVMPA = MCVAMPA[simx]