reconstruct_sc_output

Inserts the fitted pseudo-weights `w_fit` back into the original sample
data frame `sc0`, using the NA handling strategy used during
estimation. For `"omit"`, only the rows that were kept are returned.
For `"exclude"`, all original rows are returned with NA weights
for rows that were dropped. For `"fail"` or `"pass"`, all rows
are assumed to be present and weights are attached directly.

internal

Provides pseudo-weighted estimates of means and
prevalences for finite population inference from nonprobability
samples using auxiliary information from one or multiple probability
reference surveys. The package supports estimation with multiple
reference surveys, allowing auxiliary information to be combined
when no single survey contains all variables relevant to
participation. Optional cumulative precalibration can be applied to
align weighted totals of shared variables across surveys. Methods
are based on the generalized estimating equations framework of
Landsman et al. (2026) <doi:10.1002/sim.70403> for correcting
participation bias. For a single reference survey, the package
implements the raking ratio calibration method and includes the
adjusted logistic propensity (ALP) method of Wang, Valliant, and Li
(2021) <doi:10.1002/sim.9122>, as well as the Chen-Li-Wu (CLW)
method of Chen, Li, and Wu (2020) <doi:10.1080/01621459.2019.1677241>.
Analytic variance estimation uses Taylor linearization and accounts
for complex sampling designs in the reference surveys via integration
with the 'survey' package.

Jiakun Lin

nonprobsampling

Inference for Nonprobability Samples Using Multiple Reference
Surveys

Victoria Landsman

Aya A. Mitani

reconstruct_sc_output function

<dl><dt>sc0</dt>
<dd>The original sample data frame (before any NA removal).</dd>
<dt>w_fit</dt>
<dd>Numeric vector of fitted pseudo-weights, one per kept row.</dd>
<dt>keep_sc</dt>
<dd>Logical vector identifying which rows of `sc0` were retained
for fitting.</dd>
<dt>na_mode</dt>
<dd>Character string; one of `"omit"`, `"exclude"`,
`"fail"`, or `"pass"`.</dd>
<dt>na_action_obj</dt>
<dd>The `na.action` attribute produced during NA
removal, or NULL.</dd>
<dt>sc_wname</dt>
<dd>Name of the column in which pseudo-weights will be stored.</dd></dl>

Arguments

Reconstruct the sample data frame with fitted pseudo-weights — reconstruct_sc_output

<dl>

<dt>sc0</dt>
<dd>The original sample data frame (before any NA removal).</dd>


<dt>w_fit</dt>
<dd>Numeric vector of fitted pseudo-weights, one per kept row.</dd>


<dt>keep_sc</dt>
<dd>Logical vector identifying which rows of `sc0` were retained
for fitting.</dd>


<dt>na_mode</dt>
<dd>Character string; one of `"omit"`, `"exclude"`,
`"fail"`, or `"pass"`.</dd>


<dt>na_action_obj</dt>
<dd>The `na.action` attribute produced during NA
removal, or NULL.</dd>


<dt>sc_wname</dt>
<dd>Name of the column in which pseudo-weights will be stored.</dd>

</dl>

reconstruct_sc_output: Reconstruct the sample data frame with fitted pseudo-weights

Description

Usage

Value

Arguments