Henry design effect for pps sampling and GREG estimation of totals
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 y. This matrix does not include the intercept. |
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. (2013, chap. 14). Practical Tools for Designing and Weighting Survey Samples. New York: Springer.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.