corrplot(X) creates
a matrix of plots showing correlations among pairs of variables in X.
Histograms of the variables appear along the matrix diagonal; scatter
plots of variable pairs appear off diagonal. The slopes of the least-squares
reference lines in the scatter plots are equal to the displayed correlation
coefficients.

[R,PValue]
= corrplot(___) additionally returns the p-values
corresponding to the elements of R, used to test
the null hypothesis of no correlation against the alternative of a
nonzero correlation.

Load data on Canadian inflation and interest rates.

load Data_Canada

Plot the Pearson's linear correlation coefficients
between all pairs of variables.

corrplot(Dataset)

The correlation plot shows that the short-term, medium-term,
and long-term interest rates are highly correlated.

To examine the timestamp of a datum, enter gname(dates) into
the Command Window, and the software presents an interactive cross
hair over the plot. To expose the timestamp of a datum, click it using
the cross hair.

Plot Kendall's rank correlations between
multiple time series. Conduct a hypothesis test to determine which
correlations are significantly different from zero.

Load data on Canadian inflation and interest rates.

load Data_Canada

Plot the Kendall's rank correlation coefficients
between all pairs of variables. Specify a hypothesis test to determine
which correlations are significantly different from zero.

corrplot(Dataset,'type','Kendall','testR','on')

The correlation coefficients highlighted in red indicate which
pairs of variables have correlations significantly different from
zero. For these time series, all pairs of variables have correlations
significantly different from zero.

Test for correlations greater than zero between
multiple time series.

Load data on Canadian inflation and interest rates.

load Data_Canada

Return the pairwise Pearson's correlations and
corresponding p-values for testing the null hypothesis of no correlation
against the right-tailed alternative that the correlations are greater
than zero.

The output PValue has pairwise p-values all
less than the default 0.05 significance level, indicating that all
pairs of variables have correlation significantly greater than zero.

Data series that corrplot uses to plot correlations,
specified as a numObs-by-numVars matrix
or dataset array. X consists of numObs observations
made on numVars variables. plots the correlations
between the numVars variables.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments.
Name is the argument
name and Value is the corresponding
value. Name must appear
inside single quotes (' ').
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: 'tails','right','alpha',0.1 specifies
right-tailed tests at the 0.1 significance level

Variable names to be used in the plots, specified as the comma-separated
pair consisting of 'varNames' and a cell array
of strings with numVars names. All variable names
are truncated to the first five characters.

If X is a matrix, then the default
variable names are {'var1','var2',...}.

If X is a dataset array, then the
default variable names are X.Properties.VarNames.

Significance tests indicator for whether or not to test for
significant correlations, specified as the comma-separated pair consisting
of 'testR' and one of 'off' or 'on'.
If you specify the value 'on', significant correlations
are highlighted in red in the correlation matrix plot.

P-values corresponding to significance tests on the elements
of R, returned as a numVars-by-numVars matrix.
The p-values are used to test the hypothesis of no correlation against
the alternative of nonzero correlation.

The option 'rows','pairwise' 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.

P-values for Pearson's correlation are computed
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 are computed using either the exact permutation
distributions (for small sample sizes), or large-sample approximations.

P-values for two-tailed tests are computed by doubling
the more significant of the two one-tailed p-values.