similar to: bootstrap sampling using sample

Hello All, When I use bw.SJ (based on Sheather & Jones, 1991) in R to estimate the bandwidth for a highly skewed data, I get the following message: "sample is too sparse to find TD". I played around with the parameters such as no. of bins (nb), lower, upper (range over which to minimize) to no avail. My sample size is 50,000. Can anyone tell me what this means and of some

Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the

Hello; Our survey is structured as : To be investigated area is divided into 6 regions, within each region, one urban community and one rural community are randomly selected, then samples are randomly drawn from each selected uran and rural community. The problems is that in urban/rural stratum, we only have one sample. In this case, how to do bootstrap? Any comments or hints are greatly

Hi, I am having trouble understanding how to approach a simulation: I have a sample of n=250 from a population of N=2,000 individuals, and I would like to use either permutation test or bootstrap to test whether this particular sample is significantly different from the values of any other random samples of the same population. I thought I needed to take random samples (but I am not sure how

With a data frame (call it *d*) composed of 2000 individuals and *n*observations for each individual (thus *2000n* observations in total), I would like to generate *k* bootstrap samples with replacement from *d*. Amongst other variables, *d* has a numeric variable *id* taking on identical value for observations belonging to the same individual. Taking into consideration the panel nature of the

Folks, I have this code fragment: set.seed(1001) x <- c(0.79211363702017, 0.940536712079832, 0.859757602692931, 0.82529998629531, 0.973451006822, 0.92378802164835, 0.996679563355802, 0.943347739494445, 0.992873542980045, 0.870624707845108, 0.935917364493788) range(x) # from 0.79 to 0.996 e <- function(x,d) { median(x[d]) } b <- boot(x, e, R=1000)

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible Url : https://stat.ethz.ch/pipermail/r-help/attachments/20070515/799f44ed/attachment.pl

Dear experts, I am asking for help with a question regarding to stratified bootstrap. My dataset is a longitudinal dataset (3 measurements per person at year 1, 4 and 7) composed of multiple clinic centers and multiple participants within each clinic. It has missing values. I want to do a bootstrap to find the standard errors and confidence intervals for my variance components. My model is a

Hello! I am testing if my data are distributed under Laplace's distribution, which I managed to do with: > library(rmutil) > ks.test(jitter(x), "plaplace", mean(x), sd(x)) Nevertheless, I am trying to bootstrap ks.test without any success. Something is wrong in my commands: > data = read.table("data.dat",T) > library(rmutil) > library(boot) > ks.laplace =

Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

I would appreciate some thoughts on using the bootstrap functions in the library "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1)

I am fitting quantile regression models using data collected from a sample of 124 patients. When modeling cross-sectional associations, I have noticed that nonparametric bootstrap estimates of the variances of parameter estimates are much greater in magnitude than the empirical Huber estimates derived using summary.rq's "nid" option. The outcome variable is severely skewed, and I am

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and

Dear R-list, I am wondering how to perform a bootstrap in R for the weighted time dependent Cox model? (Andersen?Gill format, with multiple observations from each patients) to obtain the bootstrap standard error of the treatment effect. Below is an example dataset. Would 'censboot' be appropriate to use in this context? Any suggestions/references/direction to R-package will be highly

Hello, How do I sample observations with replacement from a normal distribution with a specific mean and s.d? (I want to see the sample, not only the statistic.) Thank you, Sigalit. [[alternative HTML version deleted]]

Hi all Forgive me, I'm a total R newbie, and this seems to be a straightforward simple bootstrap problem, but after a whole day of trying to figure out how to do it I'm ready to give up. Part of the problem is that every example and every help page seems to be about doing something more far more complex. I'm got a table with 40 columns and 750 rows. I sum all the values across the

Hi listers, I've been work on a bootstrap estimator and I don't know how to make a certain manipulation at the data. As an example I have the following data and samples... I need now to identify the vector in wich I sampled the id, I mean that I have to merge the files having the key as the id of the samples in order to find the vector that were selected. Thanks in advance, M?rcio

hi, i am using the boot package for some bootstrap calculations in place of anovas. one reason is that my dependent variable is distributed bimodally, but i would also like to learn about bootstrapping in general (i have ordered books but they have not yet arrived). i get the general idea of bootstrapping but sometimes i do not know how to define suitable statistics to test specific hypotheses.

Hi all, I am facing difficulty on how to use bootstrap sampling and below is my example of function. Read a data , use some functions and use iteration to find the solution( ie, convergence is reached). I want to use bootstrap approach to do it several times (200 or 300 times) this whole process and see the distribution of parameter of interest. Below is a small example that resembles my