svymean-m-estimator function

Weighted Huber and Tukey Mean and Total (M-Estimator) -- Robust Horvitz-Thompson Estimator

Weighted Huber and Tukey Mean and Total (M-Estimator) -- Robust Horvitz-Thompson Estimator

Weighted Huber and Tukey M-estimator of the population mean and total (robust Horvitz-Thompson estimator)

svymean_huber(x, design, k, type = "rwm", asym = FALSE, na.rm = FALSE, verbose = TRUE, ...) svytotal_huber(x, design, k, type = "rwm", asym = FALSE, na.rm = FALSE, verbose = TRUE, ...) svymean_tukey(x, design, k, type = "rwm", na.rm = FALSE, verbose = TRUE, ...) svytotal_tukey(x, design, k, type = "rwm", na.rm = FALSE, verbose = TRUE, ...)

Arguments

  • x: a one-sided [formula], e.g., ~myVariable.
  • design: an object of class survey.design; see svydesign.
  • k: [double] robustness tuning constant (0<k<=Inf0 < k <= Inf).
  • type: [character] type of method: "rwm" or "rht".
  • asym: [logical] if TRUE, an asymmetric Huber psi-function is used (default: FALSE).
  • na.rm: [logical] indicating whether NA values should be removed before the computation proceeds (default: FALSE).
  • verbose: [logical] indicating whether additional information is printed to the console (default: TRUE).
  • ...: additional arguments passed to the method (e.g., maxit: maxit number of iterations, etc.; see svyreg_control).

Details

Package survey must be attached to the search path in order to use the functions (see library or require).

  • Methods/ types: type = "rht" or type = "rwm"; see weighted_mean_huber or weighted_mean_tukey for more details.

  • Variance estimation.: Taylor linearization (residual variance estimator).

  • Utility functions: summary, coef, SE, vcov, residuals, fitted, robweights.

  • Bare-bone functions: See weighted_mean_huber

      `weighted_mean_tukey`, `weighted_total_huber`, and `weighted_total_tukey`.
    

Failure of convergence

By default, the method assumes a maximum number of maxit = 100

iterations and a numerical tolerance criterion to stop the iterations of tol = 1e-05. If the algorithm fails to converge, you may consider changing the default values; see svyreg_control.

Returns

Object of class svystat_rob

References

Hulliger, B. (1995). Outlier Robust Horvitz-Thompson Estimators. Survey Methodology 21 , 79--87.

See Also

Overview (of all implemented functions)

Examples

head(workplace) library(survey) # Survey design for stratified simple random sampling without replacement dn <- if (packageVersion("survey") >= "4.2") { # survey design with pre-calibrated weights svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight, data = workplace, calibrate.formula = ~-1 + strat) } else { # legacy mode svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight, data = workplace) } # Robust Horvitz-Thompson M-estimator of the population total svytotal_huber(~employment, dn, k = 9, type = "rht") # Robust weighted M-estimator of the population mean m <- svymean_huber(~employment, dn, k = 12, type = "rwm") # Summary statistic summary(m) # Plot of the robustness weights of the M-estimate against its residuals plot(residuals(m), robweights(m)) # Extract estimate coef(m) # Extract estimate of scale scale(m) # Extract estimated standard error SE(m)
  • Maintainer: Tobias Schoch
  • License: GPL (>= 2)
  • Last published: 2024-08-22