weighted.corr

Computes the weighted Pearson correlation coefficient of two numeric vectors.

An easy way to analyze international large-scale assessments and surveys
in education or any other dataset that includes replicated weights (Balanced Repeated
Replication (BRR) weights, Jackknife replicate weights,...). It also allows for
analyses with multiply imputed variables (plausible values). It supports the estimation
of univariate statistics (e.g. mean, variance, standard deviation, quantiles), frequencies,
correlation, linear regression and any other model already implemented in R that
takes a data frame and weights as parameters. It also includes  options to prepare
the results for publication, following the table formatting standards of the
Organization for Economic Cooperation and Development (OECD).

Rodolfo Ilizaliturri Lopez

Rrepest

An Analyzer of International Large Scale Assessments in
Education

Rodolfo Ilizaliturri

Francesco Avvisati

Francois Keslair

weighted.corr function

<dl><dt>x</dt>
<dd>(numeric vector) Variable of interest x for computing the correlation</dd>
<dt>y</dt>
<dd>(numeric vector) Variable of interest y for computing the correlation</dd>
<dt>w</dt>
<dd>(numeric vector) Vector with the weights</dd>
<dt>na.rm</dt>
<dd>(bool) if TRUE: Excludes missing values before computing the correlation</dd></dl>

Arguments

Weighted bivariate correlation — weighted.corr

<dl>

<dt>x</dt>
<dd>(numeric vector) Variable of interest x for computing the correlation</dd>


<dt>y</dt>
<dd>(numeric vector) Variable of interest y for computing the correlation</dd>


<dt>w</dt>
<dd>(numeric vector) Vector with the weights</dd>


<dt>na.rm</dt>
<dd>(bool) if TRUE: Excludes missing values before computing the correlation</dd>

</dl>

weighted.corr: Weighted bivariate correlation

Description

Usage

Value

Arguments

Examples