svyratio_huber {robsurvey}

Robust Survey Ratio M-Estimator

Description

svyratio_huber and svyratio_tukey compute the robust M-estimator of the ratio of two variables with, respectively, Huber and Tukey biweight (bisquare) psi-function.

Usage

svyratio_huber(numerator, denominator, design, k, var = denominator,
               na.rm = FALSE, asym = FALSE, verbose = TRUE, ...)
svyratio_tukey(numerator, denominator, design, k, var = denominator,
               na.rm = FALSE, verbose = TRUE, ...)

Arguments

numerator

a one-sided [formula] object (i.e., symbolic description, e.g., ~payroll).

denominator

a one-sided [formula] object (i.e., symbolic description, e.g., ~employment).

design

an object of class survey.design; see svydesign.

k

[double] robustness tuning constant (0 < k \leq \infty).

var

a [formula] object that defines the heteroscedastic variance (default: numerator).

na.rm

[logical] indicating whether NA values should be removed before the computation proceeds (default: FALSE).

asym

[logical] toggle for asymmetric Huber psi-function (default: FALSE).

verbose

[logical] indicating whether additional information is printed to the console (default: TRUE).

...

additional arguments passed to the method (e.g., maxit: maxit number of iterations, etc.).

Details

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

The functions svyratio_huber and svyratio_tukey are implemented as wrapper functions of the regression estimators svyreg_huberM and svyreg_tukeyM. See the help files of these functions (e.g., on how additional parameters can be passed via ... or on the usage of the var argument).

Value

Object of class svyreg.rob and ratio

See Also

Overview (of all implemented functions)

summary, coef, residuals, fitted, SE and vcov

plot for regression diagnostic plot methods

svyreg_huberM, svyreg_huberGM, svyreg_tukeyM and svyreg_tukeyGM for robust regression estimators

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)
    }

# Compute regression M-estimate with Huber psi-function
m <- svyratio_huber(~payroll, ~employment, dn, k = 8)

# Regression inference
summary(m)

# Extract the coefficients
coef(m)

# Extract estimated standard error
SE(m)

# Extract variance/ covariance matrix
vcov(m)

# Diagnostic plots (e.g., standardized residuals against fitted values)
plot(m, which = 1L)

# Plot of the robustness weights of the M-estimate against its residuals
plot(residuals(m), robweights(m))