similar to: You requested this report (PR#1880)

library(survival) library(boot) data=NULL lambda=NULL result=NULL pat=rep(1:102,each=1) trt=rep(c(1,0),51) status=rep(1,102) site=rep(1:51, each=2) nr.datasets=100 seed=2006 beta=log(1/2) for (i in 1:51) { lambda[i]=1+((3-1)/50)*(i-1)} lambda1=rep(lambda, each=2) dummy=rep(c(exp(beta),1),51) elf=lambda1*dummy r=70 #the number of bootstrap replicates

I'm probably not using the function right, but anyway, the program said: "please bug.report." > w_R.f1=function(PR,Pc) return (Pc*PR^3) > > w_R.f2=function(PR,Pc) return (Pc*PR) > > w_R.fc=c(w_R.f1,w_R.f2) > w_R.fc[2] [[1]] function(PR,Pc) return (Pc*PR) > > RSD(type=3, + gparP=gpar(col="red"), + gparD=gpar(fill=FALSE,col=FALSE), +

Hi, I got the following message in R: Error in as.function.default(pdfs[1]) : invalid body argument for "function" Should NEVER happen; please bug.report() [mkCLOSXP] Operating System: Windows XP (SP2) R version: R-2.0.1 Code causing error follows: # create two probability density functions for mixtures of normal distibutions fmix1 <- function(x) {dnorm(x, mean=4, sd=2) * 0.5 +

Hi guys, Looks like mkCLOSXP cannot handle external pointers as the function body. Work around is obvious, but I guess it's a bug nonetheless. > library(RGtk2) > fun <- eval(substitute(function() x, list(x = gtkWindow()))) Error in eval(expr, envir, enclos) : invalid body argument for "function" Should NEVER happen; please bug.report() [mkCLOSXP] > sessionInfo() R

Good day R help list!!! I've been trying to do Bootstrap in R on Censored data. I encountered WARNING/ERROR messages which I could not find explanation. I've been searching on the literature for two days now and still can't find answers. I hope there's anyone out there who can help me with these two questions: 1. If the "Loglik converged before variable..." message

Question: How can I get access to the bias value of a boot statistic? Details: Boot function: boot(data, statistic, R, sim="ordinary", stype="i", strata=rep(1,n), L=NULL, m=0, weights=NULL, ran.gen=function(d, p) d, mle=NULL, ...) When I create an object, containing the bootstrap statistic (object <- boot (....))I can call it and will get an output

Dear mailing list, I tried to run the example for the conditional bootstap written in the help file of censboot. I got the following result: STRATIFIED CONDITIONAL BOOTSTRAP FOR CENSORED DATA Call: censboot(data = aml, statistic = aml.fun, R = 499, F.surv = aml.s1, G.surv = aml.s2, strata = aml$group, sim = "cond") Bootstrap Statistics : original bias std. error t1*

Hello R users I am quite new to bootstrapping. Now, having some data x, ---- R: set.seed(1234) R: x <- runif(300) ---- I want to bootstrap simple statistics, mean and quantiles (.025, .975). Currently, I run a loop ---- R: res <- as.data.frame(matrix(ncol = 3, dimnames = list(NULL, ... c("M", "Lo", "Hi")))) R: for (i in 1:100) { ... y <-

Hi all, I had the need to create a colorbar considering the alpha channel of the colors, but colorRamp() and colorRampPalette() ignored the alpha argument in rgb(). So I performed some minor modifs. in their codes, as to support the interpolation using the alpha channel. I guess that those simple modifications might be useful for other people, so perhaps it would be worth to add them to

I have been trying to create code to calculate the power for an adaptive design with a survival endpoint according to the method of Schafer and Muller ('Modification of the sample size and the schedule of interim analyses in survival trials based on interim inspections,' Stats in Med, 2001). This design allows for the sample size to be increased (if necessary) based on an interim look at

Hi, i think i have figured the purpose of using this index (i-1)*5+j in the previous example that you gave. It is because that i have to consider the outer loop and inner loop also... so the iterative for i need to minus one because it have ran one times simulation already ,then times the number of sizes of inner loop, then plus the iterative of j.... then for the simulation, i think there will

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

Hi, I have a time series (say "x") of 13 years showing an evident increase. I want to exclude two observations (the fourth and 10th), so I do: > trend.test(x[-c(4,10)]) where: > x[-c(4,10)] [1] 7 37 79 72 197 385 636 705 700 1500 1900 and I get: Spearman's rank correlation rho data: x[-c(4, 10)] and time(x[-c(4, 10)]) S = 4, p-value < 2.2e-16

You have quite a few mistakes in your example. The code below works for me - you can wrap it in a function if you like. I think you will need a lot more practice before you can write something like this in R as you are missing close braces and haven't really worked out the difference between the number of calculations you are doing for each replication and the number of replications. It takes

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival) >

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) > library(survival)

This probably has a super easy answer...but I claim newbie status! (I did search help lists but this question is hard to isolate keyword-wise) Basically, I'm trying to figure out how to parse the results from executing boot(). I'm mainly interested in assigning the standard error estimate to a scalar variable. For example: --------------------------------------------------------------

Hi, The following code, from Angelo Canty article on line "Resampling Methods in R: the boot Package, 2002", works fine for Angelo Canty using R 2.6.0 on Windows XP. It also works for me using R 1.2.1 and S-PLUS 2000 on Windows XP after installing the S-PLUS bootstrap library, with slight differences in my outputs. > library(boot) >

Dear R mailing list, Greetings!!! I'm doing a graphical test of suitability for the aircondit data. I actually just followed the exercise in Davison and Hinkley's book of Graphical Test for model checking. The program for testing if it comes from an exponential distribution seems to be running okay but the program for the gamma distribution distribution is running away :-). I hope

Dear Sir, I think I found a bug in the function arma.sim, which is defined in the help page of the function filter: arma.sim <- function(n, ar = NULL, ma = NULL, sigma = 1.0) { x <- ts(rnorm(n+100, 0, sigma^2), start = -99) if(length(ma)) x <- filter(x, ma, sides=1) if(length(ar)) x <- filter(x, ar, method="recursive") as.ts(x[-(1:100)]) } I am using R