Declarative API for Staged Survey Weights

Documentation for package ‘weightflow’ version 0.1.0

Help Pages

as_svrepdesign	Export weightflow weights to a survey design
as_svydesign	Export weightflow weights to a survey design
bootstrap_estimate	Bootstrap estimate, standard error and confidence interval
bootstrap_weights	Bootstrap replicate weights that re-apply the recipe
boot_mean	Bootstrap estimate, standard error and confidence interval
boot_total	Bootstrap estimate, standard error and confidence interval
collect_replicate_weights	Collect replicate weights into a data frame ready for srvyr
collect_weights	Extract the data with the computed weights
design_effect	Kish design effect from unequal weighting
plot.prepped_weighting_spec	Diagnostic plots for the weights
population	Example target population for weightflow
prep	Estimate the weighting cascade
report_weighting	Build a nice HTML report of the weighting recipe
sample_one	Example survey sample (select-one-person, multistage)
sample_survey	Example survey sample (take-all roster)
step_assert	Assert conditions on the weights at this point of the cascade
step_calibrate	Calibration to population totals
step_drop_ineligible	Drop ineligible (out-of-scope) units
step_model_calibration	Model calibration (model-assisted, Wu & Sitter 2001)
step_nonresponse	Nonresponse adjustment
step_rescale	Rescale (normalize) the weights
step_round	Round the final weights
step_select_within	Within-household selection adjustment
step_trim	Trim extreme weights
step_trim_weights	Automatic weight trimming (survey-style)
step_unknown_eligibility	Unknown-eligibility adjustment
summary.prepped_weighting_spec	Detailed per-step diagnostics
weighting_spec	Start a weighting specification
weight_factors	Per-unit adjustment factors table
y_model	Specify a working model for a study variable y