EVER

EVER (Estimation of Variance by Efficient Replication) is an R package for calibration, estimation and sampling error assessment in complex sample surveys.

EVER’s sampling variance estimation is based on the extended DAGJK (Delete-A-group Jackknife) technique proposed by P. S. Kott (see References).

Installation

You can install the development version of EVER from GitHub as follows:

install.packages("devtools")
devtools::install_github("DiegoZardetto/EVER")

The last released version of EVER can be downloaded from Istat website.

Main Statistical Functions

  • Delete-A-Group Jackknife replication

  • Calibration of replicate weights

  • Estimates and Sampling Errors (standard error, variance, coefficient of variation, confidence interval, design effect) for:

    • Totals
    • Means
    • Absolute and relative frequency distributions
    • Ratios between totals
    • Multiple regression coefficients
    • Quantiles

  • Estimates and Sampling Errors for user-defined Complex Estimators (even non-analytic)

  • Estimates and Sampling Errors for Subpopulations (Domains)

    • All the analyses above can be carried out for arbitrary domains

Sampling Variance Estimation Methodology

The advantage of the DAGJK method over the traditional jackknife is that, unlike the latter, it remains computationally manageable even when dealing with “complex and big” surveys (tens of thousands of PSUs arranged in a large number of strata with widely varying sizes). In fact, the DAGJK method is known to provide, for a broad range of sampling designs and estimators, (near) unbiased standard error estimates even with a “small” number (e.g. a few tens) of replicate weights.

Besides its peculiar computational efficiency, the DAGJK method takes advantage of the strong points it shares with the most common replication methods. As a remarkable example, EVER is designed to fully exploit DAGJK’s versatility: the package provides the user with a user-friendly tool for calculating estimates, standard errors and confidence intervals for estimators defined by the user themselves (even non-analytic). This functionality makes EVER especially appealing whenever variance estimation by Taylor linearisation can be applied only at the price of crude approximations (e.g. poverty estimates).

References

Kott, Phillip S. (1999) “The Extended Delete-A-Group Jackknife”. Bulletin of the International Statistical Instititute. 52nd Session. Contributed Papers. Book 2, pp. 167-168.

Kott, Phillip S. (2001) “The Delete-A-Group Jackknife”. Journal of Official Statistics, Vol.17, No.4, pp. 521-526.

Disclaimer

In case you come across malfunctions or flaws of this website, please bear in mind that it has been automatically generated from the sources of the EVER package and it has no human maintainers.

In particular, the printed output in the ‘Examples’ sections of some functions - e.g. kottby.user() - is known to mistakenly show error messages that do not actually exist in the package.