From this paradigm, using weights inversely proportional to sampling probabilities is (primarily?) a tool for finite population inference -- what Deming called an 'enumerative study'. For an enumerative study, the purpose is to make inference about a fixed, finite population, e.g., how to feed the people in Japan who would otherwise starve within the next week or month, which was the situation when Deming directed a survey there shortly after World War II. For an analytic study, the purpose is more long term, e.g., how to design a national alimentary system to feed the people who will be there 10 or 30 years from now. Since most of my work has dealt processed that will create the future, rather than dealing with fixed, finite populations, I have ignored sampling probabilities in most of my work (though I have not worked much recently with sample surveys).
Is this still consistent with current thinking? Is it feasible to summarize in a few words what Pferrermann, Korn et al. say about this?
Thanks,
spencer gravesThomas Lumley wrote:
On Thu, 20 May 2004, Baskin, Robert wrote:
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.
I would have thought that it matters why you are fitting a mixed model. Often people use mixed models when they are just interested in inference about the mean and need to model the covariances to get valid standard errors. In that situation you could use an ordinary survey regression to get a design-based result.
If you are actually interested in variance components then you need some other approach, and putting the weights into the model as a covariate will presumably give a valid model-based result (since the weights carry all the biased sampling information --- like a propensity score). Presumably this is also more efficient.
However, it could well be that you don't want those variables in the model. If the sampling depends on a variable Z correlated with Y and X and you want to model the distribution of Y given X, not the distribution of Y given X and Z, you are still in trouble.
-thomas
(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:[EMAIL PROTECTED] Sent: Wednesday, May 19, 2004 12:47 PM To: [EMAIL PROTECTED] 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
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Thomas Lumley Assoc. Professor, Biostatistics [EMAIL PROTECTED] University of Washington, Seattle
______________________________________________
[EMAIL PROTECTED] mailing list
https://www.stat.math.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
