svymean_trimmed function

Weighted Trimmed Mean and Total

Weighted trimmed population mean and total.

svymean_trimmed(x, design, LB = 0.05, UB = 1 - LB, na.rm = FALSE, ...)
svytotal_trimmed(x, design, LB = 0.05, UB = 1 - LB, na.rm = FALSE, ...)

Arguments

• x: a one-sided [formula], e.g., ~myVariable.
• design: an object of class survey.design; see svydesign.
• 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 $\leq 1$.
• na.rm: [logical] indicating whether NA values should be removed before the computation proceeds (default: FALSE).
• ...: additional arguments (currently not used).

Details

Package survey must be attached to the search path in order to use the functions (see library or require).

• Characteristic.: Population mean or total. Let $\mu$ denote the estimated trimmed population mean; then, the estimated trimmed 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.: Large-sample approximation based on the influence function; see Huber and Ronchetti (2009, Chap. 3.3) and Shao (1994).

• Utility functions.: summary, coef, SE, vcov, residuals, fitted, robweights.

• Bare-bone functions.: See weighted_mean_trimmed and weighted_total_trimmed.

Returns

Object of class svystat_rob

References

Huber, P. J. and Ronchetti, E. (2009). Robust Statistics, New York: John Wiley and Sons, 2nd edition. tools:::Rd_expr_doi("10.1002/9780470434697")

Shao, J. (1994). L-Statistics in Complex Survey Problems. The Annals of Statistics 22 , 976--967. tools:::Rd_expr_doi("10.1214/aos/1176325505")

See Also

Overview (of all implemented functions)

weighted_mean_trimmed and weighted_total_trimmed

Examples

head(workplace)

library(survey)
# Survey design for stratified simple random sampling without replacement
dn <- if (packageVersion("survey") >= "4.2") {
        # survey design with pre-calibrated weights
        svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
                  data = workplace, calibrate.formula = ~-1 + strat)
    } else {
        # legacy mode
        svydesign(ids = ~ID, strata = ~strat, fpc = ~fpc, weights = ~weight,
                  data = workplace)
    }

# Estimated trimmed population total (5% symmetric trimming)
svytotal_trimmed(~employment, dn, LB = 0.05, UB = 0.95)

# Estimated trimmed population mean (5% trimming at the top of the distr.)
svymean_trimmed(~employment, dn, UB = 0.95)