Fix bug in #15, where
bootstrap conversion of multistage survey design objects with
as_bootstrap_design() would throw an error when user
manually specified weights in svydesign().
Creation of Rao-Wu-Yue-Beaumont bootstrap replicate weights is now faster and takes less computer memory.
Typo fix in vignettes.
This release adds several functions for creating bootstrap and generalized bootstrap replicate weights. The new vignette “Bootstrap methods for surveys” provides guidance for choosing a bootstrap method and selecting the number of bootstrap replicates to use, along with statistical details and references.
Added function as_bootstrap_design() to convert a
survey design object to a replicate design with replicate weights
created using a bootstrap method. This is essentially a specialized
version of as.svrepdesign() that supports additional
bootstrap methods and has detailed documentation about which bootstrap
methods can be used for different types of sampling designs.
Added function as_gen_boot_design() to convert a
survey design object to a replicate design with replicate weights
created using the generalized survey bootstrap. The user must supply the
name of a target variance estimator (e.g., “Horvitz-Thompson” or
“Ultimate Cluster”) used to create the generalized bootstrap factors.
See the new vignette for details.
Added functions to help choose the number of bootstrap
replicates. The function estimate_boot_sim_cv() can be used
to estimate the simulation error in a bootstrap estimate caused by using
a finite number of bootstrap replicates. The new function
estimate_boot_reps_for_target_cv() estimates the number of
bootstrap replicates needed to reduce the simulation error to a target
level.
Added function make_rwyb_bootstrap_weights(), which
creates bootstrap replicate weights for a wide range of survey designs
using the method of Rao-Wu-Yue-Beaumont (i.e., Beaumont’s generalization
of the Rao-Wu-Yue bootstrap method). This function can be used directly,
or users can specify
as_bootstrap_design(type = "Rao-Wu-Yue-Beaumont").
Added function make_gen_boot_factors() to create
replicate adjustment factors using the generalized survey bootstrap. The
key input to make_gen_boot_factors() is the matrix of the
quadratic form used to represent a variance estimator. The new function
make_quad_form_matrix() can be used to represent a chosen
variance estimator as a quadratic form, given information about the
sample design. This can be used for stratified multistage SRS designs
(with or without replacement), systematic samples, and PPS samples, with
or without replacement.
Minor Updates and Bug Fixes:
as_data_frame_with_weights(), ensure that
the full-sample weight is named "FULL_SAMPLE_WGT" if the
user does not specify something different.calibrate_to_estimate(), ensure that the output
names the list of columns with perturbed control columns
col_selection instead of
perturbed_control_cols, so that the name matches the
corresponding function argument, col_selection.Added helper function as_data_frame_with_weights()
to convert a survey design object into a data frame with columns of
weights (full-sample weights and, if applicable, replicate weights).
This is useful for saving data and weights to a data file.
Added by argument to
summarize_rep_weights() which allows the specification of
one or more grouping variables to use for summaries
(e.g. by = c('stratum', 'response_status') can be used to
summarize by response status within each stratum).
Added a small vignette “Nonresponse Adjustments” to illustrate
how to conduct nonresponse adjustments using
redistribute_weights().
Minor Updates and Bug Fixes:
rho in calibrate_to_estimate().stack_replicate_designs() where designs
created with as.svrepdesign(..., type = 'mrbbootstrap') or
as.svrepdesign(..., type = 'subbootstrap') threw an
error.Added functions calibrate_to_estimate() and
calibrate_to_sample() for calibrating to estimated control
totals with methods that account for the sampling variance of the
control totals. For an overview of these functions, please see the new
vignette “Calibrating to Estimated Control Totals”.
The function calibrate_to_estimate() requires the
user to supply a vector of control totals and its variance-covariance
matrix. The function applies Fuller’s proposed adjustments to the
replicate weights, in which control totals are varied across replicates
by perturbing the control totals using a spectral decomposition of the
control totals’ variance-covariance matrix.
The function calibrate_to_sample() requires the user
to supply a replicate design for the primary survey of interest as well
as a replicate design for the control survey used to estimate control
totals for calibration. The function applies Opsomer & Erciulescu’s
method of varying the control totals across replicates of the primary
survey by matching each primary survey replicate to a replicate from the
control survey.
Added an example dataset, lou_vax_survey, which is a
simulated survey measuring Covid-19 vaccination status and a handful of
demographic variables, based on a simple random sample of 1,000
residents of Louisville, Kentucky with an approximately 50% response
rate.
lou_pums_microdata provides
person-level microdata from the American Community Survey (ACS)
2015-2019 public-use microdata sample (PUMS) data for Louisville, KY.
The dataset lou_pums_microdata includes replicate weights
to use for variance estimation and can be used to generate control
totals for lou_vax_survey.