From: Wittner, Ben, Ph.D. <Wittner.Ben_at_mgh.harvard.edu>

Date: Thu, 3 Jan 2008 09:41:39 -0500

##

## version information

##

R-help_at_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. Received on Thu 03 Jan 2008 - 14:45:22 GMT

Date: Thu, 3 Jan 2008 09:41:39 -0500

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 <Jose.Pinheiro_at_pharma.novartis.com>, Douglas

Bates <bates_at_stat.wisc.edu>, Saikat DebRoy <saikat_at_stat.wisc.edu>, Deepayan Sarkar <Deepayan.Sarkar_at_R-project.org> the R Core team.Maintainer: R-core <R-core_at_R-project.org> 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)

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