# corrplot

Plot variable correlations

## Syntax

## Description

`[`

plots Pearson's correlation coefficients
between all pairs of variables in the input matrix of time series data. The plot is a
`R`

,`PValue`

]
= corrplot(`X`

)`numVars`

-by-`numVars`

grid, where
`numVars`

is the number of time series variables (columns) in the data,
including the following subplots:

Each off diagonal subplot contains a scatterplot of a pair of variables with a least-squares reference line, the slope of which is equal to the displayed correlation coefficient.

Each diagonal subplot contains the distribution of a variable as a histogram.

Also, the function returns the correlation matrix in the plots and a matrix
of *p*-values for testing the null hypothesis that each pair of
coefficients is not correlated against the alternative hypothesis of a nonzero
correlation.

`[`

plots the Pearson's correlation coefficients between all pairs of variables in the input
table or timetable, and also returns tables for the correlation matrix and matrix of
`R`

,`PValue`

] = corrplot(`Tbl`

)*p*-values.

To select a subset of variables, for which to plot the correlation matrix, use the
`DataVariables`

name-value argument.

`[___] = corrplot(___,`

specifies options using one or more name-value arguments in
addition to any of the input argument combinations in previous syntaxes.
`Name=Value`

)`corrplot`

returns the output argument combination for the
corresponding input arguments. For example,
`corrplot(Tbl,Type="Spearman",TestR="on",DataVariables=1:5)`

computes
Spearman’s rank correlation coefficient for the first 5 variables of the table
`Tbl`

and tests for significant correlation coefficients.

`corrplot(___)`

plots the correlation matrix.

`corrplot(`

plots on the axes specified by `ax`

,___)`ax`

instead of
the current axes (`gca`

). `ax`

can precede any of the input
argument combinations in the previous syntaxes.

`[___,`

plots the diagnostics of the input series and
additionally returns handles to plotted graphics objects. Use elements of
`H`

]
= corrplot(___)`H`

to modify properties of the plot after you create it.

## Examples

## Input Arguments

## Output Arguments

## Tips

The setting

`Rows="pairwise"`

(the default) can return a correlation matrix that is not positive definite. The setting`Rows="complete"`

returns a positive-definite matrix, but, in general, the estimates are based on fewer observations.

## Algorithms

`corrplot`

computes*p*-values for Pearson’s correlation by transforming the correlation to create a*t*-statistic with`numObs`

– 2 degrees of freedom. The transformation is exact when the input time series data is normal.`corrplot`

computes*p*-values for Kendall’s and Spearman’s rank correlations by using either the exact permutation distributions (for small sample sizes) or large-sample approximations.`corrplot`

computes*p*-values for two-tailed tests by doubling the more significant of the two one-tailed*p*-values.

## Version History

**Introduced in R2012a**