weighted_mean_trimmed function

Weighted Trimmed Mean and Total (bare-bone functions)

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

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 $0 \leq$ 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 $Nhat \mu$ 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`.
  

Returns

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_trimmed and svytotal_trimmed

Examples

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)