weighted_mean_trimmed: Weighted Trimmed Mean and Total (bare-bone functions)
In robsurvey: Robust Survey Statistics Estimation

weighted_mean_trimmed

R Documentation

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)

Arguments

`x`	`[numeric vector]` data.
`w`	`[numeric vector]` weights (same length as `x`).
`LB`	`[double]` lower bound of trimming such that 0 ≤q `LB` < `UB` ≤q 1.
`UB`	`[double]` upper bound of trimming such that 0 ≤q `LB` < `UB` ≤q 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 μ denote the estimated trimmed population mean; then, the estimated trimmed population total is given by Nhat μ with Nhat = 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.

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]

Examples

data(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)

robsurvey documentation built on Jan. 6, 2023, 5:09 p.m.