Declarative Recipes for Staged Survey Weighting with Recipe-Aware Replicate Variances


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Documentation for package ‘weightflow’ version 1.1.0

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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 Recipe-aware bootstrap replicate weights
boot_mean Bootstrap estimate, standard error and confidence interval
boot_total Bootstrap estimate, standard error and confidence interval
collect_propensities Recover the fitted response propensities of a nonresponse step
collect_replicate_weights Collect replicate weights into a data frame ready for srvyr
collect_step_detail Per-unit detail of one step of the cascade
collect_weights Extract the data with the computed weights
design_effect Kish design effect from unequal weighting
domain_summary Per-domain weight summary at every stage of the cascade
jackknife_estimate Jackknife estimate, standard error and confidence interval
jackknife_weights Recipe-aware delete-a-PSU jackknife replicate weights
jack_mean Jackknife estimate, standard error and confidence interval
jack_total Jackknife estimate, standard error and confidence interval
plot.prepped_weighting_spec Diagnostic plots for the weights
population Synthetic target population (sampling frame)
prep Estimate the weighting cascade
print.weightflow_boot Print a bootstrap replicate-weight object
print.weightflow_jack Print a jackknife replicate-weight object
report_weighting Self-contained HTML quality report for a weighting recipe
sample_one Synthetic address sample with one selected person per household
sample_survey Synthetic person sample with a take-all household roster
step_assert Assert quality conditions on the weights
step_calibrate Calibration to population totals
step_drop_ineligible Drop ineligible (out-of-scope) units
step_model_calibration Model-assisted calibration (Wu and Sitter 2001)
step_nonresponse Nonresponse adjustment
step_rescale Rescale the weights to a fixed sum
step_round Round the final weights
step_select_within Within-cluster selection adjustment
step_trim Trim extreme weights against a ratio
step_trim_calibrated Trimmed calibration (range-restricted, totals-preserving)
step_trim_weights Automatic weight trimming to an absolute band
step_unknown_eligibility Unknown-eligibility adjustment
summary.prepped_weighting_spec Detailed per-step diagnostics
weightflow-concepts Conventions shared by every weightflow step
weighting_spec Start a weighting specification
weight_factors Per-unit adjustment factors table
y_model Specify a working model for a study variable y