View source: R/svyratio_huber.R
svyratio_huber | R Documentation |
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.
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, ...)
numerator |
a one-sided |
denominator |
a one-sided |
design |
an object of class |
k |
|
var |
a |
na.rm |
|
asym |
|
verbose |
|
... |
additional arguments passed to the method (e.g., |
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).
Object of class svyreg.rob
and ratio
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
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))
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