**From:** Baskin, Robert (*RBaskin@ahrq.gov*)

**Date:** Fri 21 May 2004 - 00:25:03 EST

**Next message:**Thomas Lumley: "RE: [R] mixed models for analyzing survey data with unequal selec tion probability"**Previous message:**Rajarshi Guha: "Re: [R] column sorting a matrix with indices returned"**Next in thread:**Thomas Lumley: "RE: [R] mixed models for analyzing survey data with unequal selec tion probability"**Reply:**Thomas Lumley: "RE: [R] mixed models for analyzing survey data with unequal selec tion probability"

Message-id: <6BCD3F430455B1418750004BCD27925905C6D6@exchange2.ahrq.gov>

Han-Lin

I don't think I have seen a reply so I will suggest that maybe you could try

a different approach than what you are thinking about doing. I believe the

current best practice is to use the weights as a covariate in a regression

model - and bytheway - the weights are the inverse of the probabilities of

selection - not the probabilities.

Fundamentally, there is a difficulty in making sense out of 'random effects'

in a finite population setting.

(plagiarized from some unknown source)

See: < 9. Pfeffermann, D. , Skinner, C. J. , Holmes, D. J. , Goldstein, H. ,

and Rasbash, J. (1998), ``Weighting for unequal selection probabilities in

multilevel models (Disc: p41-56)'', Journal of the Royal Statistical

Society, Series B, Methodological, 60 , 23-40 >

which refers back to:

<29. Pfeffermann, D. , and LaVange, L. (1989), ``Regression models for

stratified multi-stage cluster samples'', Analysis of Complex Surveys,

237-260 >

If you don't like statistical papers, then see section 4.5 of <8. Korn,

Edward Lee , and Graubard, Barry I. (1999), ``Analysis of health surveys'',

John Wiley & Sons (New York; Chichester) > They explain the idea of using

weights in a model fairly simply.

Bob

-----Original Message-----

From: Han-Lin Lai [mailto:Han-Lin.Lai@noaa.gov]

Sent: Wednesday, May 19, 2004 12:47 PM

To: r-help@stat.math.ethz.ch

Subject: [R] mixed models for analyzing survey data with unequal selection

probability

Hi,

I need the help on this topic because this is out of my statistical

trianing as biologist. Here is my brief description of the problem.

I have a survey that VESSELs are selected at random with the probability

of p(j). Then the tows within the jth VESSEL are sampled at random with

probability of p(i|j). I write my model as

y = XB + Zb + e

where XB is fixed part, Zb is for random effect (VESSEL) and e is

within-vessel error.

I feel that I should weight the Zb part by p(j) and the e-part by

p(i,j)=p(j)*p(i|j). Is this a correct weighting?

How can I implement the weightings in nlme (or lme)? I think that

p(i,j) can be specified by nlme(..., weights=p(i,j),...)? Where is p(j)

to be used in nlme?

I appreciate anyone can provide examples and literature for this

problem.

Cheers!

Han

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