Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs.
With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model, used intervals() to get a 95% CI for beta, and then checked whether the the CI contained beta. The rate at which the CI did not contain beta was 8%, which was greater than the 5% I was expecting. This may seem like a small difference, but in the lab in which I work M would more likely be 2 or 3. When I re-ran with M = 3 I got 13% of the CIs not containing beta and when I re-ran with M = 2, I got 21%. Am I calculating the CIs incorrectly? Am I interpreting them incorrectly? Am I doing anything else wrong? Output of packageDescription('nlme') and version given below the code. Any help will be greatly appreciated. Thanks very much in advance. -Ben ######################################################################### ## ## Code to test intervals() based on equations (1.4) and (1.5) of ## Pinheiro and Bates ## library('nlme') M <- 6 n <- 3 beta <- 67 sigma.b <- 25 sigma <- 4 Rail <- rep(1:M, each=n) set.seed(56820) B <- 10000 num.wrong <- 0 error.fraction <- Ks <- c() for (K in 1:B) { travel <- beta + rep(rnorm(M, sd=sigma.b), each=n) + rnorm(M*n, sd=sigma) fm1Rail.lme <- lme(travel ~ 1, random = ~ 1 | Rail) CI <- intervals(fm1Rail.lme, which='fixed')$fixed if ((CI[1, 'lower'] > beta) || (CI[1, 'upper'] < beta)) num.wrong <- num.wrong + 1 if (K %% 200 == 0) { error.fraction <- c(error.fraction, num.wrong/K) Ks <- c(Ks, K) plot(Ks, error.fraction, type='b', ylim=range(c(0, 0.05, error.fraction))) abline(h=0.05, lty=3) } } num.wrong/B ######################################################################### ## ## version information ## > packageDescription('nlme') Package: nlme Version: 3.1-86 Date: 2007-10-04 Priority: recommended Title: Linear and Nonlinear Mixed Effects Models Author: Jose Pinheiro <[EMAIL PROTECTED]>, Douglas Bates <[EMAIL PROTECTED]>, Saikat DebRoy <[EMAIL PROTECTED]>, Deepayan Sarkar <[EMAIL PROTECTED]> the R Core team. Maintainer: R-core <[EMAIL PROTECTED]> Description: Fit and compare Gaussian linear and nonlinear mixed-effects models. Depends: graphics, stats, R (>= 2.4.0) Imports: lattice LazyLoad: yes LazyData: yes License: GPL (>=2) Packaged: Thu Oct 4 23:25:21 2007; hornik Built: R 2.6.0; i686-pc-linux-gnu; 2007-12-26 15:48:00; unix -- File: /home/bwittner/R-2.6.0/library/nlme/DESCRIPTION > version _ platform i686-pc-linux-gnu arch i686 os linux-gnu system i686, linux-gnu status major 2 minor 6.0 year 2007 month 10 day 03 svn rev 43063 language R version.string R version 2.6.0 (2007-10-03) The information transmitted in this electronic communication is intended only for the person or entity to whom it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of or taking of any action in reliance upon this information by persons or entities other than the intended recipient is prohibited. If you received this information in error, please contact the Compliance HelpLine at 800-856-1983 and properly dispose of this information. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.