You can do this with a few steps:
In multivariate platform or principle components platform, display the correlation matrix.
Then right-click the matrix and select "Make into Data Table".
Resulting in this new table
You will have to do some manipulation of that table of correlations:
Use Tables > Stack to create a tall version of the table that 3 columns: A column with the variable names to use for categories on the X axis, another column with categories for the Y axis, and a column with correlation of the variables in the previous two columns.
Now you can use the Graph Builder to create the heat map of these correlations. I did have to reverse the order of the variables on the X axis in the axis settings to get what you wanted.
Here's a script that recreates all those steps on the sample data table ("Body Measurements.JMP") that I used to illustrate all of this.
dt = Open( "$SAMPLE_DATA/Body Measurements.jmp" );
pca = dt << Principal Components(
Y(
:Mass,
:Fore,
:Bicep,
:Chest,
:Neck,
:Shoulder,
:Waist,
:Height,
:Calf,
:Thigh,
:Head
),
Correlations( 1 ),
);
dtcorr = Report( pca )["Correlations"][Matrix Box( 1 )] <<
Make Into Data Table;
dtcorrstacked = dtcorr << Stack(
columns(
:Mass,
:Fore,
:Bicep,
:Chest,
:Neck,
:Shoulder,
:Waist,
:Height,
:Calf,
:Thigh,
:Head
),
Source Label Column( "VariableYAxis" ),
Stacked Data Column( "Correlation" )
);
dtcorrstacked << Set Name( "Stacked Correlations" );
Close( dtcorr, NoSave );
Column( dtcorrstacked, "Row" ) << Set Name( "VariableXAxis" );
dtcorrstacked << Graph Builder(
Size( 531, 531 ),
Show Control Panel( 0 ),
Graph Spacing( 4 ),
Variables(
X( :VariableXAxis ),
Y( :VariableYAxis ),
Color( :Correlation )
),
Elements( Heatmap( X, Y, Legend( 6 ) ) ),
SendToReport(
Dispatch( {}, "VariableXAxis", ScaleBox, {Reversed Scale} )
)
);