svymean_trimmed {robsurvey}

Weighted Trimmed Mean and Total

Description

Weighted trimmed population mean and total.

Usage

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).

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 \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.

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.

Value

Object of class svystat_rob

References

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

Shao, J. (1994). L-Statistics in Complex Survey Problems. The Annals of Statistics 22, 976–967. 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)