robsurvey-package {robsurvey} | R Documentation |
Package Overview
Description
A key design pattern of the package is that the majority of the estimating methods is available in two "flavors":
bare-bone methods
survey methods
Bare-bone methods are stripped-down versions of the survey methods in terms of functionality and informativeness. These functions may serve users and package developers as building blocks. In particular, bare-bone functions cannot compute variances.
The survey methods are much more capable and depend, for variance estimation, on the survey package.
Basic Robust Estimators
Trimming
Bare-bone methods:
weighted_mean_trimmed
andweighted_total_trimmed
Survey methods:
svymean_trimmed
andsvytotal_trimmed
Winsorization
Bare-bone methods:
Survey methods:
Dalen's estimators (weight reduction methods)
Bare-bone methods:
weighted_mean_dalen
andweighted_total_dalen
Survey methods:
svymean_dalen
andsvytotal_dalen
M-estimators
Bare-bone methods:
-
huber2
(weighted Huber Proposal 2 estimator)
Survey methods:
-
mer
(minimum estimated risk estimator)
Survey Regression (weighted least squares)
Robust Regression and Ratio Estimation (weighted)
Regression M-estimators:
svyreg_huberM
andsvyreg_tukeyM
Regression GM-estimators (Mallows and Schweppe):
svyreg_huberGM
andsvyreg_tukeyGM
Ratio M-estimators:
svyratio_huber
andsvyratio_tukey
Note: The functions svyreg_huber
and
svyreg_tukey
are deprecated, use instead
svyreg_huberM
and svyreg_tukeyM
, respectively;
see also robsurvey-deprecated.
Robust Generalized Regression (GREG) and Ratio Prediction of the Population Mean and Total
Regression predictors:
svymean_reg
andsvytotal_reg
Ratio predictors:
svymean_ratio
andsvytotal_ratio