[R] nlme question
Doran, Harold
HDoran at air.org
Thu Nov 17 17:05:06 CET 2005
James,
By assumption, sigma and tau are assumed uncorrelated, which I believe
Deepayan noted below. Sigma is also random error so it is uncorrelated
with your fixed effects. What are the covariance terms you are seeking?
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Wassell, James
T., Ph.D.
Sent: Thursday, November 17, 2005 10:29 AM
To: r-help at stat.math.ethz.ch
Subject: [R] nlme question
-----Original Message-----
From: Wassell, James T., Ph.D.
Sent: Thursday, November 17, 2005 9:40 AM
To: 'Deepayan Sarkar'
Subject: RE: nlme question
Deepayan,
Thanks for your interest. It's difficult in email but I need the
variance of Kappa = mu + 1.645*tau + 1.645* sigma
Just using the standard variance calculation Var(K) = Var(mu) +
(1.645)^2 * Var(tau) + 1.645^2 * var(sigma) + [three covariance terms
with constant multipliers].
So, I can get var(mu) [or actually the standard error] from the summary
function -- and var(tau) and var(sigma) using the VarCorr function, but
I still need the covariance terms.
I am trying to duplicate methods in a paper by Nicas & Neuhaus,
"Variability in Respiratory Protection and the Assigned Protection
Factor" J. Occ & Environ Health, Feb. 2004. p 99-109. (see eqn. 12).
The authors used Proc Mixed, but I can't figure out how to get
covariance terms with SAS either.
Thanks again.
Terry Wassell
-----Original Message-----
From: Deepayan Sarkar [mailto:deepayan.sarkar at gmail.com]
Sent: Thursday, November 17, 2005 2:52 AM
To: Wassell, James T., Ph.D.
Cc: r-help at stat.math.ethz.ch
Subject: Re: nlme question
On 11/16/05, Wassell, James T., Ph.D. <jtw2 at cdc.gov> wrote:
> I am using the package nlme to fit a simple random effects (variance
> components model)
>
> with 3 parameters: overall mean (fixed effect), between subject
> variance (random) and within subject variance (random).
So to paraphrase, your model can be written as (with the index i
representing subject)
y_ij = \mu + b_i + e_ij
where
b_i ~ N(0, \tao^2)
e_ij ~ N(0, \sigma_2)
and all b_i's and e_ij's are mutually independent. The model has, as you
say, 3 parameters, \mu, \tao and \sigma.
> I have 16 subjects with 1-4 obs per subject.
>
> I need a 3x3 variance-covariance matrix that includes all 3 parameters
> in order to compute the variance of a specific linear combination.
Can you specify the 'linear combination' that you want to estimate in
terms of the model above?
Deepayan
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