Clustered data with Design package--bootcov() vs. robcov()

Hi,

I am trying to figure out exactly what the bootcov() function in the Design
package is doing within the context of clustered data. From reading the
documentation/source code it appears that using bootcov() with the cluster
argument constructs standard errors by resampling whole clusters of
observations with replacement rather than resampling individual
observations. Is that right, and is there any more detailed documentation on
the math behind this? Also, what is the difference between these two
functions:

Correct.  Did you read the Feng et al reference in bootcov's help file 
or check the book that is related to the package?

bootcov(my.model, cluster.id)
robcov(my.model, cluster.id)

robcov does not use bootstrapping.  It uses the cluster sandwich 
(Huber-White) variance-covariance estimator for which there are 
references in the help file (see especially Lin).

Both robcov and bootcov work best when there is a large number of small 
clusters.  If the clusters are somewhat large and greatly vary in size, 
expect to be in trouble and consider a full modeling approach 
(generalized least squares, mixed models, etc.).

One advantage of robcov is that you get the same result every time, 
unlike bootstrapping.  But even in the case of cluster sizes of one, the 
sandwich estimator can be inefficient (see the Gould paper) or can 
result in the "right" estimates of the "wrong" quantity (see a paper by 
Friedman in American Statistician).

Frank

Thank you.

Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University