weighted_mean_trimmed: Weighted Trimmed Mean and Total (bare-bone functions)

Description

Weighted trimmed mean and total (bare-bone functions with limited functionality; see svymean_trimmed and svytotal_trimmed for more capable methods)

Usage

weighted_mean_trimmed(x, w, LB = 0.05, UB = 1 - LB, info = FALSE,
                      na.rm = FALSE)
weighted_total_trimmed(x, w, LB = 0.05, UB = 1 - LB, info = FALSE,
                       na.rm = FALSE)

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]

Arguments

x: [numeric vector] data.
w: [numeric vector] weights (same length as x).
LB: [double] lower bound of trimming such that \(0 \leq\) LB \(<\) UB \(\leq 1\).
UB: [double] upper bound of trimming 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).

Details

Characteristic.

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

Trimming.

The methods trims the LB\(~\cdot 100\%\) of the smallest observations and the (1 - UB)\(~\cdot 100\%\) of the largest observations from the data.

Variance estimation.

See survey methods:

svymean_trimmed,
svytotal_trimmed.

Examples

Run this code

head(workplace)

# Estimated trimmed population total (5% symmetric trimming)
weighted_total_trimmed(workplace$employment, workplace$weight, LB = 0.05,
    UB = 0.95)

# Estimated trimmed population mean (5% trimming at the top of the distr.)
weighted_mean_trimmed(workplace$employment, workplace$weight, UB = 0.95)