similar to: Correction in error

I have the following code, gibbs <-function(m,theta = 0.25, lambda =0.55, n =1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:(m+1)){ x[t,1] <- rbinom(1, x[t-1,3], x[t-1,1]) x[t,2]<-rbeta(1, x[t-1,1] + alpha, x[t-1,3] - x[t-1,1] + beta) x[t,3]

I have got following error in drawing wavelet fitting. can some one help? > library(faraway) > data(lidar) > newlidar<-lidar[c(1:128),] > library(wavethresh) > wds <- wd(newlidar$logratio) > draw(wds) Error in plot.default(x = x, y = zwr, main = main, sub = sub, xlab = xlab, : formal argument "type" matched by multiple actual arguments [[alternative HTML

Hi all I have a function of log defined by y = log(1- exp(-a)), when exp(-a) is greater, 1, it produce NaN. How can I remove this in R? [[alternative HTML version deleted]]

Hi group, I imported 16 data frames using the function "list.files" temp <- list.files(path="...........") myfiles = lapply(temp, read.table,sep = "") Now I have 16 data set imported in R window. I want to combine them by row and tried some thing like (Here I am considering only 20 columns) for(i in 1:16){ data<- cbind(myfiles[[i]][,1:20]) } but it

Hi all, I have the data from the posterior distribution for some parameter. I want to find the 95% credible interval. I think "t.test(data)" is only for the confidence interval. I did not fine function for the Bayesian credible interval. Could some one suggest me? Thanks [[alternative HTML version deleted]]

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

There is a problem with `rpois'. It does seem to take care about the order of the arguments. This is an example: > rpois(n=1,lambda=2) [1] 3 > rpois(lambda=2,n=1) [1] 2 0 It obviously uses the first argument as the number of samples to be drawn, which is wrong. I used Version 0.49 Beta (April 23, 1997). Fredrik

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However,

Hi all, why factorial(150) shows the error out of range in 'gammafn'? I have to calculate the number of subset formed by 150 samples taking 10 at a time. How is this possible? best [[alternative HTML version deleted]]

Hi R community, I have some data set and construct the likelihood as follows likelihood <- function(alpha,beta){ lh<-1 d<-0 p<-0 k<-NULL data<-read.table("epidemic.txt",header = TRUE) attach(data, warn.conflicts = F) k <-which(inftime==1) d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta) p<-1 - exp(-alpha*d) for(i in 1:100){

I'd appreciate it if you'd keep on list for the archives. That said, I think this function does what you were hoping for. Michael powerset <- function(n, items = NULL){ if(!is.null(items)) { if(n != length(items)) warning("Resetting n in preference to length(items)") n = length(items) } smat <- do.call(expand.grid, rep(list(c(0,1)), n))

Hi R community! I am analyzing the data set "motorins" in the package "faraway" by using the generalized additive model. it shows the following error. Can some one suggest me the right way? library(faraway) data(motorins) motori <- motorins[motorins$Zone==1,] library(mgcv) >amgam <- gam(log(Payment) ~ offset(log(Insured))+ s(as.numeric(Kilometres)) + s(Bonus) + Make +

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),       

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Hi all, I have a set of elements (1, 1, 0,1,1,0,1,0,1,1) with ten elements. I have to construct the permutation matrix of this set which is of the size 10 by 2^10. Can some one help how is this possible? Is there is a particular function in R or I need to make function? Best [[alternative HTML version deleted]]

Hi all, I have the following code, When I run the code, it never terminate this is because of the while loop i am using. In general, if you need a loop for which you don't know in advance how many iterations there will be, you can use the `while' statement so here too i don't know the number how many iterations are there. So Can some one suggest me whats going on? I am using the

Hi all, I got following problem in fitting the data. Any kind of suggestions are welcome > beta <- 3.5 > d <- seq(0.1,62.5,0.1) > y <- exp(-beta*d) > y1 <- y > x <- read.table("epidist.txt", header = TRUE) > data.nls <- as.data.frame(cbind(y1,x)) > #attach(data.nls) > nls.fit <- nls(y1~dist,data.nls) Error in cll[[1L]] : object of type

Hi, R Devils, I'm running the actual R version in JGR (version 1.5-8 ). Sys.getlocale(category = "LC_ALL") yields [1] "LC_COLLATE=German_Germany.1252;LC_CTYPE=German_Germany.1252;LC_MONETARY=German_Germany.1252;LC_NUMERIC=C;LC_TIME=German_Germany.1252" I want to write some HTML-Code enhanced by statistical results and labels encoded in Latin-1, which I pass to a function.

I have a problem in where i generate m independent draws from a binomial distribution, say draw1 = rbinom( m , size.a, prob.a ) then I need to use each draw to generate a beta distribution. So, like using a beta prior, binomial likelihood, and obtain beta posterior, m many times. I have not found out a way to vectorize draws from a beta distribution, so I have an explicit for loop

I couldn't find out how the following to solve: There is a column in a data.frame: > d[, 'Y6'] [1] 6 3 8 11 8 9 6 8 3 5 10 15 NA 9 8 3 8 16 6 6 NA 10 5 2 7 7 6 16 7 15 7 10 12 8 7 12 12 16 7 6 8 8 15 6 [45] NA 8 99 7 12 8 9 16 7 16 8 7 7 1 15 12 8 7 10 7 8 7 8 9 8 6 6 8 6 16 11 5 11 11 1 11 3 7 7 10 10 10 6 11 [89] 16 NA