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Hi, I using the boot package 1.2-20 on R 2.0.1. My statistics function estimates 6 parameters. In a small percentage of resampled data sets my statistics function doesn't produce an estimate for one parameter and the boot function stops with an error. I can write an ifelse(exists('parameter.estimate'), parameter.estimate, NA) statement within the statistic function to substitute

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

Running under Redhat 7.3 Linux I have installed R Version 1.8.0 (2003-10-08) from a binary download using the rpm manager. Using packageStatus(), I am attempted to upgrade several packages. This is unsuccessful with the following error messages: * Installing *source* package 'MASS' ... ** libs gcc -I/usr/lib/R/include -I/usr/local/include -D__NO_MATH_INLINES -mieee-fp -fPIC -O2

Hi All, I'm new at programming in R. Some functions create objects for which extractor functions are written to pull out some partial result. I wish to extract partial results from functions without extractor functions: for example, to pass a vector of results to another function. Even after looking at V&R S Programming I don't see a general approach. Specifically, how would I

To all: I currently download the R binaries for Redhat 7.x Linux. There is considerable turmoil in the vendors of Linux. Redhat apparently is changing it's business model to paid versions. This might motivate my department to use a different vendor of Linux. Is there anything predictable about which vendors/versions of Linux will have R binaries in the future? Thanks, Nathan PS I

Hi, Has anyone written code for isotonic regression on ordered rectangular grids? Nathan Nathan Leon Pace, MD, MStat University of Utah Salt Lake City, UT 84132 Office: 801.581.6393 Fax: 801.581.4367 Cell: 801.558.3987 Pager: 801.291.9019 Home: 801.467.2925 [[alternative text/enriched version deleted]]

This is a question of clarification. IN 2009 Higgins, Thompson and Spiegelhalter (J R Statist Soc A 172:137-159) gave WinBUGs code to get credible intervals from random effects meta analysis for the prediction interval of a new study. It appears that the predict.rma function creates approximate credible intervals (pending a function revision by the author) for that purpose. Is my assumption

Hi All, I have a four panel xyplot. I wish to plot each point as an open or filled circle depending on the value of an indicator variable. I assume I need to use panel.superpose(), but I can't figure out the syntax from lattice documentation. Running R 2.1 under Mac OS X 10.4.2. Any suggestions would be appreciated. Nathan Nathan Leon Pace, MD, MStat University of Utah Salt Lake

Hi I'm using boot package for some analysis on linear regression coeficients. My problem is that I can not compute bca intervals, I get an error message > bca.ci(blm8901,index=1) Error in if (!all(rk > 1 & rk < R)) warning("Extreme Order Statistics used as Endpoints") : missing value where logical needed The problem is the empinf.reg function that is

Dear All, I am trying to use the bca.ci function on a boot.out object which consists a few NaN's and I want to ignore those NaN's, and get a ci only for the "normal" values. boot.out$t has R number of values for 3000 different statistics, so when I use boot.ci(boot.out, index=i) and i happens to be a column in boot.out$t with some NaN's in there I get an error message. I

Hello everyone ! I am currently trying to convert a program from S-plus to R, and I am having some trouble with the S-plus function called "influence(data, statistic,...)". This function aims to "calculate empirical influence values and related quantities", and is part of the Resample library that I cannot find for R. However, 2 similar functions are available in R: - the

Dear R-Experts, Thanks to Prof. Bonnett, I have got an R script working to calculate confidence intervals around the semipartial correlation coefficients. Now, I would like to calculate BCa bootstrap CIs using the boot library and the boot.ci(results, type="all") function. How could I modify my R script (here below reproducible example) to get the BCa bootstrap CIs ? CIsemipartcorr

Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with

Hi, I've got a linear mixed model created using lmer: A6mlm <- lmer(Score ~ division + (1|school), data=Age6m) (To those of you to whom this model looks familiar, thanks for your patience with this & my other questions.) Anyway, I was trying this to look at the significance of my fixed effects: A6post <- mcmcsamp(A6mlm, 50000) library(coda) HPDinterval(A6post) ..but I got this

Hi, I was wondering: I've got a dataset where I've got student 'project's nested within 'school's, and 'division' (elementary, junior, or senior) at the student project level. (Division is at the student level and not nested within schools because some students are registered as juniors & others as seniors within the same school.) So schools are random,

HI everyone, I try to get some bootstrap CIs for coefficients obtained by quantile regression. I have influencial values and thus switched to quantreg.. The data is clustered and within clusters the variance of my DV = 0.. Is this sensible for the below data? And what about the warnings? Thanks in advance for any guidance, Kay > dput(d) structure(list(Porenfläche = c(4990L, 7002L, 7558L,

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

Dear Friends, I'm trying to learn to use parallel computation using snow & rpvm Now, I have two boxes: 1) Pentium 4, pvm 3.4.5, R 2.2.0 with FreeBSD 5.4 (box uffbsd); 2) PowerPC, pvm 3.4.5, R 2.1.1 with Debian Linux latest stable (box powerpclinux); I'm using the instructions in (found in snow.pdf) http://www.stat.uiowa. edu/~luke/R/cluster/cluster.html. Now ** from the box

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

Hello, First of all, when I tried to use function mic I got an error. mic(cbind(C, D)) Error in mic(cbind(C, D)) : could not find function "mic" So I've changed your function myCor and all went well, with a warning relative to BCa intervals. myCor <- function(data, index){ mine(data[index, ])$MIC } results=boot(data = cbind(C,D), statistic = myCor, R = 2000)