Weighted Huber and Tukey M-estimator of the mean and total
(bare-bone function with limited functionality; see
svymean_huber, svymean_tukey,
svytotal_huber, and svytotal_tukey for more
capable methods)
weighted_mean_huber(x, w, k, type = "rwm", asym = FALSE, info = FALSE,
na.rm = FALSE, verbose = TRUE, ...)
weighted_total_huber(x, w, k, type = "rwm", asym = FALSE, info = FALSE,
na.rm = FALSE, verbose = TRUE, ...)
weighted_mean_tukey(x, w, k, type = "rwm", info = FALSE, na.rm = FALSE,
verbose = TRUE, ...)
weighted_total_tukey(x, w, k, type = "rwm", info = FALSE, na.rm = FALSE,
verbose = TRUE, ...)The return value depends on info:
info = FALSE:estimate of mean or total [double]
info = TRUE:a [list] with items:
characteristic [character],
estimator [character],
estimate [double],
variance (default: NA),
robust [list],
residuals [numeric vector],
model [list],
design (default: NA),
[call]
[numeric vector] data.
[numeric vector] weights (same length as x).
[double] robustness tuning constant
(\(0 < k \leq \infty\)).
[character] type of method: "rwm" or
"rht"; see below (default: "rwm").
[logical] toggle for asymmetric Huber psi-function
(default: FALSE).
[logical] indicating whether additional information
should be returned (default: FALSE).
[logical] indicating whether NA values should
be removed before the computation proceeds (default: FALSE).
[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.).
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.
Population mean or total. Let \(\mu\) denote the estimated population mean; then, the estimated total is given by \(\hat{N} \mu\) with \(\hat{N} =\sum w_i\), where summation is over all observations in the sample.
Two methods/types are available for estimating the location \(\mu\):
type = "rwm" (default):robust weighted
M-estimator of the population mean and total,
respectively. This estimator is recommended for sampling
designs whose inclusion probabilities are not
proportional to some measure of size. [Legacy note: In an
earlier version, the method type = "rwm" was called
"rhj"; the type "rhj" is now silently
converted to "rwm"]
type = "rht":robust Horvitz-Thompson M-estimator of the population mean and total, respectively. This estimator is recommended for proportional-to-size sampling designs.
See the related but more capable functions:
svymean_huber and
svymean_tukey,
svytotal_huber and
svytotal_tukey.
By default, the Huber or Tukey
psi-function are used in the specification of the M-estimators. For
the Huber estimator, an asymmetric version of the Huber
psi-function can be used by setting the argument
asym = TRUE in the function call.
Hulliger, B. (1995). Outlier Robust Horvitz-Thompson Estimators. Survey Methodology 21, 79--87.
Overview (of all implemented functions)
head(workplace)
# Robust Horvitz-Thompson M-estimator of the population total
weighted_total_huber(workplace$employment, workplace$weight, k = 9,
type = "rht")
# Robust weighted M-estimator of the population mean
weighted_mean_huber(workplace$employment, workplace$weight, k = 12,
type = "rwm")
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