weighted.cor2

Computes a matrix of weighted correlations between the columns of <code>x</code> and the columns of <code>y</code>. This can be Pearson, Spearman or Kendall correlation.

 multivariate

 misc 

Description of statistical associations between two variables : measures of local and global association between variables (phi, Cramér V, correlations, eta-squared, Goodman and Kruskal tau, permutation tests, etc.), multiple graphical representations of the associations between two variables (using 'ggplot2') and weighted statistics.

Nicolas Robette

descriptio

Descriptive Statistical Analysis

weighted.cor2 function

<dl> <dt>x</dt>
<dd>a data frame of numeric vectors</dd> <dt>y</dt>
<dd>an optional data frame of numeric vectors. Default is NULL, which means that correlations between the columns of <code>x</code> are computed.</dd> <dt>weights</dt>
<dd>numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used.</dd> <dt>method</dt>
<dd>a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman".</dd> <dt>na.rm</dt>
<dd>logical, indicating whether NA values should be silently removed before the computation proceeds. Default is FALSE.</dd></dl>

Arguments

Author

Weighted correlations — weighted.cor2

<dl>

 <dt>x</dt>
<dd>a data frame of numeric vectors</dd>

 <dt>y</dt>
<dd>an optional data frame of numeric vectors. Default is NULL, which means that correlations between the columns of <code>x</code> are computed.</dd>

 <dt>weights</dt>
<dd>numeric vector of weights. If NULL (default), uniform weights (i.e. all equal to 1) are used.</dd>

 <dt>method</dt>
<dd>a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman".</dd>

 <dt>na.rm</dt>
<dd>logical, indicating whether NA values should be silently removed before the computation proceeds. Default is FALSE.</dd>

</dl>

weighted.cor2: Weighted correlations

Description

Usage

Value

Arguments

Author

See Also

Examples