weighted_mean_winsorized {robsurvey}

Weighted Winsorized Mean and Total (bare-bone functions)

Description

Weighted winsorized mean and total (bare-bone functions with limited functionality; see svymean_winsorized and svytotal_winsorized for more capable methods)

Usage

weighted_mean_winsorized(x, w, LB = 0.05, UB = 1 - LB, info = FALSE,
                         na.rm = FALSE)
weighted_mean_k_winsorized(x, w, k, info = FALSE, na.rm = FALSE)
weighted_total_winsorized(x, w, LB = 0.05, UB = 1 - LB, info = FALSE,
                          na.rm = FALSE)
weighted_total_k_winsorized(x, w, k, info = FALSE, na.rm = FALSE)

Arguments

x

[numeric vector] data.

w

[numeric vector] weights (same length as x).

LB

[double] lower bound of winsorization such that 0 \leq LB < UB \leq 1.

UB

[double] upper bound of winsorization such that 0 \leq LB < UB \leq 1.

info

[logical] indicating whether additional information should be returned (default: FALSE).

na.rm

[logical] indicating whether NA values should be removed before the computation proceeds (default: FALSE).

k

[integer] number of observations to be winsorized at the top of the distribution.

Details

Characteristic.

Population mean or total. Let \mu denote the estimated winsorized population mean; then, the estimated population total is given by \hat{N} \mu with \hat{N} =\sum w_i, where summation is over all observations in the sample.

Modes of winsorization.

The amount of winsorization can be specified in relative or absolute terms:

• Relative: By specifying LB and UB, the methods winsorizes the LB~\cdot 100\% of the smallest observations and the (1 - UB)~\cdot 100\% of the largest observations from the data.

• Absolute: By specifying argument k in the functions with the "infix" _k_ in their name, the largest k observations are winsorized, 0<k<n, where n denotes the sample size. E.g., k = 2 implies that the largest and the second largest observation are winsorized.

Variance estimation.

See survey methods:

• svymean_winsorized,

• svytotal_winsorized,

• svymean_k_winsorized,

• svytotal_k_winsorized.

Value

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]

See Also

Overview (of all implemented functions)

svymean_winsorized, svymean_k_winsorized, svytotal_winsorized and svytotal_k_winsorized

Examples

head(workplace)

# Estimated winsorized population mean (5% symmetric winsorization)
weighted_mean_winsorized(workplace$employment, workplace$weight, LB = 0.05)

# Estimated one-sided k winsorized population total (2 observations are
# winsorized at the top of the distribution)
weighted_total_k_winsorized(workplace$employment, workplace$weight, k = 2)