R help - Apr 2006 - Bootstrap and Jackknife Bias using Survey Package

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?

2. How to see each replicate estimate of the parameter?

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?

Thanks before hand to anyone how can help me!

Carlos Creva Singano (M2004078) wrote:
> 
> 1. How to compute Bootstrap and Jackknife Bias of estimates, like mean?
Have you had a look at packages "boot" and "bootstrap"? E.g.
you can
compute the bias and s.e. of an estimate theta using bootstrap

library("boot")
a <- boot( data, theta, R=1000 )

where boot is a function of the boot package, and theta is a function
that you can define yourself. In your case, it is not necessary and you
can use mean instead of theta.
> 2. How to see each replicate estimate of the parameter?
In the example above, they can be found in the boot object "a" in:

a$t

-- 
Ram?n Casero Ca?as

http://www.robots.ox.ac.uk/~rcasero/wiki
http://www.robots.ox.ac.uk/~rcasero/blog

Hi 
 you should use the package boot to compute bootsrap and jacknife 
 


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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

On Tue, 11 Apr 2006, justin bem wrote:
> Hi
> you should use the package boot to compute bootsrap and jacknife
>
Not if he wants to get the right answers.  This was for a stratified 
multistage cluster-sampled survey.

 	-thomas

Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle