Plot variable correlations

`corrplot(X)`

`corrplot(X,Name,Value)`

`R = corrplot(___)`

```
[R,PValue]
= corrplot(___)
```

`corrplot(ax,___)`

```
[R,PValue,H]
= corrplot(___)
```

`corrplot(`

creates a matrix of
plots showing correlations among pairs of variables in `X`

)`X`

.
Histograms of the variables appear along the matrix diagonal; scatter plots of
variable pairs appear in the off diagonal. The slopes of the least-squares
reference lines in the scatter plots are equal to the displayed correlation
coefficients.

`corrplot(`

uses additional options specified by one or more name-value pair arguments. For
example, `X`

,`Name,Value`

)`corrplot(X,'type','Spearman','testR','on')`

computes
Spearman’s rank correlation coefficient and tests for significant correlation
coefficients.

returns the correlation matrix of `R`

= corrplot(___)`X`

displayed in the plots
using any of the input argument combinations in the previous syntaxes.

`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.

The option

`'rows','pairwise'`

, which is the default, can return a correlation matrix that is not positive definite. The`'complete'`

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

`gname`

to identify points in the plots.

The software 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`X`

is normal.*p*-values for Kendall’s and Spearman’s rank correlations using either the exact permutation distributions (for small sample sizes) or large-sample approximations.*p*-values for two-tailed tests by doubling the more significant of the two one-tailed*p*-values.

`collintest`

| `corr`

| `gname`