On Tue, 11 Apr 2006, Carlos Creva Singano (M2004078) wrote:
Dear R users,
I´m student of Master in Statistic and Data analysis, in New University
of Lisbon. And now i´m writting my dissertation in variance
estimation.So i´m using Survey Package to compute the principal
estimators and theirs variances.
My data is from Incoming and Expendire Survey. This is stratified
Multi-stage Survey care out by National Statistic Institute of
Mozambique. My domain of analysis is Maputo City, the Capital of
Mozambique. I just compute the sampling errors using Survey Package, but
i have some droubles:
1. How to compute Bootstrap and Jackknife Bias of estimates, like mean?
I don't know how to do this for survey estimates, but if you do, you can
compute it from the replicates.
2. How to see each replicate estimate of the parameter?
All the survey functions for replicate designs will return the replicates
with the return.replicates=TRUE option
R> a <- svymean(~api00,rclus1,return.replicates=TRUE)
R> summary(a$replicates)
Min. 1st Qu. Median Mean 3rd Qu. Max.
636.1 640.4 642.9 644.3 645.7 667.7
R> str(a)
List of 2
$ mean : atomic [1:1] 644
..- attr(*, "var")= num 693
..- attr(*, "statistic")= chr "mean"
$ replicates: num [1:15] 643 648 646 643 645 ...
- attr(*, "class")= chr "svrepstat"
3. Is it possible to use the Sitter algoritm in Bootstrap for stratified
and multi-stage sampling in Survey Package? Or is possible to use the
pseudo-population (obtained by replication the sample) in estimation
using Bootstrap?
as.svrepdesign( ,type="bootstrap")
does create a pseudo-population in designs with a finite population
correction. This is as described in the reference by Davison and Canty
given on the help page.
I don't know what you mean by the "Sitter algorithm". Prof Sitter has
written several papers on bootstrapping survey data -- can you give a more
precise reference?
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
[EMAIL PROTECTED] University of Washington, Seattle______________________________________________
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