[R] Bootstrap and Jackknife Bias using Survey Package
Thomas Lumley
tlumley at u.washington.edu
Tue Apr 11 17:14:24 CEST 2006
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
tlumley at u.washington.edu University of Washington, Seattle
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