deffH | R Documentation |

Compute the Henry design effect for single-stage samples when a general regression estimator is used for a total.

deffH(w, y, x)

`w` |
vector of inverses of selection probabilities for a sample |

`y` |
vector of the sample values of an analysis variable |

`x` |
matrix of covariates used to construct a GREG estimator of the total of |

The Henry design effect is the ratio of the variance of the general regression (GREG) estimator of a total of *y* to the variance of the estimated total in *srswr*. Calculations for the Henry *deff* are done as if the sample is selected in a single-stage and with replacement. Varying selection probabilities can be used. The model for the GREG is assumed to be *y = α + β x + ε*, i.e., the model has an intercept.

numeric design effect

Richard Valliant, Jill A. Dever, Frauke Kreuter

Henry, K.A., and Valliant, R. (2015). A Design Effect Measure for Calibration Weighting in Single-stage Samples. *Survey Methodology*, 41, 315-331.

Valliant, R., Dever, J., Kreuter, F. (2018, chap. 14). *Practical Tools for Designing and Weighting Survey Samples, 2nd edition*. New York: Springer.

`deff`

, `deffCR`

, `deffK`

, `deffS`

set.seed(-500398777) # generate population using HMT function pop.dat <- as.data.frame(HMT()) mos <- pop.dat$x pop.dat$prbs.1d <- mos / sum(mos) # select pps sample require(sampling) n <- 80 pk <- n * pop.dat$prbs.1d sam <- UPrandomsystematic(pk) sam <- sam==1 sam.dat <- pop.dat[sam, ] dsgn.wts <- 1/pk[sam] deffH(w=dsgn.wts, y=sam.dat$y, x=sam.dat$x)

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