similar to: Bootstrapping the colMeans statistic

I wish to perform moving tiles bootstrap resampling on some gridded data meteorological data. I've many years experience with S-Plus, but it has no way to perform a moving-tiles bootstrap. Within R I've learned how to use quadratresample() with the spatstats package and would be happy to simply use empirical percentiles if generating the replicates were fast, but it isn't. So,

Hi, I am trying to run some bootstraps with the boot package. When I run it with 400 replicates it does it ok, but then I need to run the same analysis but with 89, 86, 102 and 106 samples (for four different environments), and then is when I get the error message: > mybootstrap <- boot(Datos, mystat, 2000) Error in t.star[r, ] <- statistic(data, i[r, ], ...) : number of items to

Having searched and searched I still haven't found what's the problem with my data (I've attached the relevant file). Every time I tried to use the CANCOR-Function I got error messages. So I turned to check my two sets of variables separately by using the "colMeans"-Function. With one set no problem .. but with the other. My input : > struktur <-

Small data sets (6-12 values, or a similarly small number of groups) which don't look nice and symmetric are quite common in my field (analytical chemistry and biological variants thereof), and often contain outliers or at least stragglers that I cannot simply discard. One of the things I occasionally do when I want to see what different assumptions do to my confidence intervals is to run a

Hi all, I would like to know if it is posible by, someway, to get colMeans from a data.frame with numeric as well as character data, dispersed all over the object. Note that I would like to get colMeans neglecting character data. I am really in need of some function proceeding in that way… All the best Diogo André Alagador [[alternative HTML version deleted]]

Dear Lillian, I tried to estimate parameters for time series regression using time series bootstrapping as described on page 434 in Davison & Hinkley (1997) - bootstrap methods and their application. This approach is based on an AR process (ARIMA model) with a regression term (compare also with page 414 in Venable & Ripley (2002) - modern applied statistics with S) I rewrote the code

Hello, I am have fitted GLS models to time series data. Now I wish to bootstrap this data to produce confidence intervals for the model. However, because this is time series data, normal bootstrapping is not applicable. Secondly, 'tsboot' appears to only be useful for ar models - and does not seem to be applicable to GLS models. I have written code in R to randomly sample blocks of

There are indeed speed advantages in using colSums etc. However the disadvantage is that the newbie doesn't always find the power inherent in the apply, sapply, tapply and mapply. For many things that I do, the speed is the least of my worries; although I take the point that using apply for means or sums in packages that are distibuted to others is not the way to go. As many of us have found

I have executed a Repeated Measures ANOVA with one DV (latency) and one within subject factor (acoustic condtion: 3 levels) by bootstrapping my sampling distribution of F from the empirical sample distribution. I chose to resample because the sample distribution deviates from normality a lot. The overall F is significant and now I wish to decompose this with contrasts to ask if latencies to

Dear All, I was writing a small wrapper to bootstrap a classification algorithm, but if we generate the indices in the "usual way" as: bootindex <- sample(index, N, replace = TRUE) there is a non-zero probability that all the samples belong to only one class, thus leading to problems in the fitting (or that some classes will end up with only one sample, which will be a problem

Insightful are pleased to announce we are now taking bookings for our latest course on "Bootstrap methods and permutation tests" in the UK on 23rd - 24th October. This course will focus in particular on two resampling methods, bootstrapping and permutation tests, which have been applied successfully to areas of statistical modelling where "traditional" standard errors,

I read from external data source containing several columns. Each column represents value of a metric. The columns are time series data. I want to sort the resulting dataframe such that the column with the largest mean is the leftmost column, descending in colMean values to the right. I see many solutions for sorting rows based on some column characteristic, but haven't found any

[text file is also attached in case you find the format of email difficult to understand] Dear useRs,You must all the planning for the christmas, but i am stucked in my office on the following issue i had a file containg information about station name, year, month, day, and discharge information. i opened it by using following command > dat1<-read.table("EL.csv",header=TRUE,

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) >

Hello, I think I found a bug in rsync. I checked the TODO and the issues, but I didn't find any reference to it. It seems, that rsync goofes up, if you rename a directory and then create a symlink with the old name to the new location. Below is a simple example how to do it. ------------------------------------------------------------------------------ [ptriller] elmore:~/test > ls

Hello all, I have 300 clusters of fixed size 2 and I need to do resampling with replacement to run some regression models. I've searched through the archive and the only thing I found was that I could use 'boot()' function. but it's not clear to me how I can do this. Has anyone done it? I would very much appreciate your help, kelly

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with

Hi, I'd like some advice on bootstrapping in R. I have a species x with 20 individuals and a factor containing 0 and 1's (in this case 5 zeros and 15 ones). I want to compare the frequency of the occurrence of 1 with a probability value. This code seems to work to do this in R. attach(test) p <- c(0.5272, (1-0.5272)) sp1_1 <- length(subset(x, x==1)) sp1_0 <- length(subset(x,

There is now a tutorial on bootstrapping and other resampling methods at: http://www.burns-stat.com/pages/Tutor/bootstrap_resampling.html Corrections and other suggestions are welcome. The project started because a novice asked me about bootstrapping. My response was, "How dare you bug me while I'm playing with my cats, just google for it." My correspondent was not very impressed

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with