similar to: Bootstrapping / stats question (not R)

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

Dear R Help List, I have a vector of n and a vector of n-1 and I want to use boot() to bootstrap the ratio of their respective medians. I want to eventually use boot.ci() to generate confidence intervals. Because the vectors are not equal in length, I tried a few things, but have yet to be successful. Try 1: > x <- runif(20) > > y <- c(runif(19), NA) > > median(x) [1]

I am running R 2.7.0 on a Suse 9.1 linux cluster with a job scheduler dispatching jobs and openmpi-1.0.1. I have tried running one of the examples at http://ace.acadiau.ca/math/ACMMaC/Rmpi/examples.html in Rmpi and they seem to be working, except mpi.close.Rslaves() hangs. The slaves are closed, but the master doesn't finish its script. Below is the example script and the call to R. The job is

Hi, I'm testing the efficiency of the Rmpi package regarding parallelization using a cluster. I've found and tried the task pull programming method, but even if it is described as the best method, it seems to cause deadlock, anyone could help me in using this method? here is the code I've found and tried: # Initialize MPI library("Rmpi") # Notice we just say "give us

Dear experienced R-users, I am having some probably trivial trouble estimating the confidence interval for the difference of two group means, with groups been of unequal sample size. I am using the "Bootstrap" package and the function "bcanon"(bcanon(x, nboot, theta, ...,alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975)) for Nonparametric BCa confidence limits. The

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the */retain/ *statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the retain statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2 which

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Dear R users, Version 0.30-6 of the np package has been uploaded to CRAN. See http://cran.r-project.org/package=np Note that the cubature package is now required in addition to the boot package. The recent updates in 0.30-4 through 0.30-6 provides additional functionality in the form of five new functions that incorporate frequently requested nonparametric entropy-based testing methods to the

Dear all, Could anyone help me figure out why bootstrap works for one of similar models but not for the other and how I can solve it? I am using R 2.11.1 in Windows and would like to get confidence intervals for my models A and B by bootstrapping. However, bootstrap gives expected output for the model A but not for B, which I found was puzzling because the structure of the models is

I am trying to (semi) calculate the confidence intervals for a loess smoother (function: loess()), but have been thus far unsuccessful. The CI for the loess predicted values, yhat, are apparently yhat +- t*s * sqrt(w^2), where s is the residual sum of squares and w is the weight function Correct me of I'm wrong, but R uses the tricubic function (1-abs(z)^3)^3, where z = (x-xi)/h, where h

Dear all, So far i could do (in an informal way) to draw a Standardized Resisual plot in the following way- --------------------- >x <- c(104.1, 106.6, 105.5, 107.5, 109.6, 113.3, 115.5, 117.7, 119.9, 122.1, 124.3, 126.5, 128.2) >y <- c(53732, 52912, 57005, 61354, 67682, 71602, 71961, 75309, 82931, 93310, 102161, 103068, 108927) >

I know that there are two method to apply the Hosmer and Lemeshow?s. One of them is calculated based on the fixed and pre-determined cut-off points of the estimated probability of success. One of them is calculated based on the percentiles of estimated probabilities. In the previous post,i find that the Hosmer and Lemeshow?s test how to use in R. hosmerlem <- function (y, yhat, g = 10) {

Sorry for the repeat. The PRESS statistic is defined as sum(y-yhat(i))^2, where yhat(i) denotes the ith predicted value using all the data except the ith case (as used typically in linear models). Thanks again Jacob Jacob L van Wyk Department of Mathematics and Statistics Rand Afrikaans University P O Box 524 Auckland Park 2006 South Africa Tel: +27-11-489-3080 Fax: +27-11-489-2832

Dear List, I am having trouble with some R code I have written to perform Redundancy Analysis (RDA) on a matrix of species abundance data (Y) and a matrix of environmental data (X). RDA is a constrained form of PCA and can be thought of as a PCA of the fitted values of a regression of each variable in Y on all variables in X. For info, the first use of RDA is in: Rao, C.R, 1964. The use and

Hello all: Please help me with this "can't find object" issue. I'm trying to get leave-one-out predicted values for Beta-binomial regression. It may be the gamlss issue because the code seems to work when %do% is used. I have searched for similar issues, but haven't managed to figure it out. This is on Windows 10 platform. Thanks in advance, Nik #

Dear users, I'm trying to implement the nonparametric "two-stage" bootstrap (Davison and Hinkley 1997, pag 100-102) in R. As far as I understood, 'bootcov' is the most appropriate method to implement NONPARAMETRIC bootstrap in R when you have clustered data (?). I read the 'bootcov' manual but I still have a few questions: 1 - When the variable 'cluster' is

hello, the following questions will without doubt reveal some fundamental ignorance, but hopefully you can still help me out. I'd like to bootstrap a coefficient gained on the basis of the coefficients in a logistic regression model (the mean differences in the predicted probabilities between two groups, where each predict() operation uses as the newdata-argument a dataframe of equal size as