extract

Returns a <code>data.frame</code> of estimated mean(s) or standard error(s)

Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.

Maciej Ber<c4><99>sewicz

nonprobsvy

Inference Based on Non-Probability Samples

Łukasz Chrostowski

Maciej Beręsewicz

Piotr Chlebicki

extract function

<dl><dt>object</dt>
<dd>object of of the <code>nonprob</code> class</dd>
<dt>what</dt>
<dd>what to extract: all estimates (mean(s), SE(s) and CI(s); <code>"all"</code>; default), estimated mean(s) (<code>"mean"</code>) or their standard error(s) (<code>"se"</code>)</dd></dl>

Arguments

Extracts Estimates from the Nonprob Class Object — extract

<dl>

<dt>object</dt>
<dd>object of of the <code>nonprob</code> class</dd>


<dt>what</dt>
<dd>what to extract: all estimates (mean(s), SE(s) and CI(s); <code>"all"</code>; default), estimated mean(s) (<code>"mean"</code>) or their standard error(s) (<code>"se"</code>)</dd>

</dl>

extract: Extracts Estimates from the Nonprob Class Object

Description

Usage

Value

Arguments

Examples