Message-ID: <1347479733.24756.41.camel@milan>
Date: 2012-09-12T19:55:33Z
From: Milan Bouchet-Valat
Subject: boot() with glm/gnm on a contingency table
In-Reply-To: <yajfehm78ham.fsf@gmail.com>
Le mercredi 12 septembre 2012 ? 07:08 -0700, Tim Hesterberg a ?crit :
> One approach is to bootstrap the vector 1:n, where n is the number
> of individuals, with a function that does:
> f <- function(vectorOfIndices, theTable) {
> (1) create a new table with the same dimensions, but with the counts
> in the table based on vectorOfIndices.
> (2) Calculate the statistics of interest on the new table.
> }
>
> When f is called with 1:n, the table it creates should be the same
> as the original table. When called with a bootstrap sample of
> values from 1:n, it should create a table corresponding to the
> bootstrap sample.
Indeed, that's another solution I considered, but I wanted to be sure
nothing more reasonable exists. You're right that it's more efficient
than replicating the whole data set. But still, with a typical table of
less than 100 cells and several thousands of observations, this means
creating a potentially long vector, much larger than the original data;
nothing really hard with common machines, to be sure.
If no other way exists, I'll use this. Thanks.