bootClassic

Simple proportion statistics function for bootstrap estimation

The blocked weighted bootstrap (BBW) is an estimation technique
for use with data from two-stage cluster sampled surveys in which either
prior weighting (e.g. population-proportional sampling or PPS as used in
Standardized Monitoring and Assessment of Relief and Transitions or SMART
surveys) or posterior weighting (e.g. as used in rapid assessment method or
RAM and simple spatial sampling method or S3M surveys) is implemented. See
Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of
bootstrap to cluster samples. See Aaron et al (2016)
<doi:10.1371/journal.pone.0163176> and Aaron et al (2016)
<doi:10.1371/journal.pone.0162462> for application of the blocked weighted
bootstrap to estimate indicators from two-stage cluster sampled surveys.

Ernest Guevarra

Blocked Weighted Bootstrap

Mark Myatt

bootClassic function

<dl><dt>x</dt>
<dd>A data frame with primary sampling unit (PSU) in column named
<code>psu</code> and with data column/s containing the binary variable/s (0/1) of
interest with column names corresponding to <code>params</code> values</dd>
<dt>params</dt>
<dd>A vector of column names corresponding to the binary variables
of interest contained in <code>x</code></dd></dl>

Arguments

Simple proportion statistics function for bootstrap estimation — bootClassic

<dl>

<dt>x</dt>
<dd>A data frame with primary sampling unit (PSU) in column named
<code>psu</code> and with data column/s containing the binary variable/s (0/1) of
interest with column names corresponding to <code>params</code> values</dd>


<dt>params</dt>
<dd>A vector of column names corresponding to the binary variables
of interest contained in <code>x</code></dd>

</dl>

bootClassic: Simple proportion statistics function for bootstrap estimation

Description

Usage

Value

Arguments

Examples