survey (version 2.4)

svyCprod: Computations for survey variances

Description

Computes the sum of products needed for the variance of survey sample estimators.

Usage

svyCprod(x, strata, psu, fpc, nPSU,
      lonely.psu=getOption("survey.lonely.psu"))

Arguments

x

A vector or matrix

strata

A vector of stratum indicators, or NULL

psu

A vector of cluster indicators or NULL

fpc

A data frame of population stratum sizes or NULL

nPSU

Table of original sample stratum sizes (or NULL)

lonely.psu

One of "remove", "adjust", "fail", "certainty". See Details below

Value

• A covariance matrix

Details

The observations for each cluster are added, then centred within each stratum and the outer product is taken of the row vector resulting for each cluster. This is added within strata, multiplied by a degrees-of-freedom correction and by a finite population correction (if supplied) and added across strata.

If there are fewer clusters (PSUs) in a stratum than in the original design extra rows of zeroes are added to x to allow the correct subpopulation variance to be computed.

The variance formula gives 0/0 if a stratum contains only one sampling unit. The options to handle this are "fail" to give an error, "remove" or "certainty" to give a variance contribution of 0 for the stratum, and "adjust" to center the stratum at the grand mean rather than the stratum mean. The choice is controlled by setting options(survey.lonely.psu). If this is not done the factory default is "fail". Using "adjust" is conservative, and it would often be better to combine strata in some intelligent way.

The "remove"and "certainty" options give the same result, but "certainty" is intended for situations where there is only one PSU in the population stratum, which is sampled with certainty (also called `self-representing' PSUs or strata). With "certainty" no warning is generated for strata with only one PSU. The factory default is "fail".

See Also

svydesign, svy.varcoef