Aquí hay un script que debería ser un buen comienzo para crear lo que quieres

Names Default To Here( 1 );
// Open a sample data table
dt = Open( "$SAMPLE_DATA/semiconductor capability.jmp" );
// Run the Correlation Platform to get the data
mult = dt << Multivariate(
invisible,
Y( :NPN1, :PNP1, :PNP2, :NPN2, :PNP3, :IVP1 ),
Estimation Method( "Row-wise" ),
Matrix Format( "Square" ),
Scatterplot Matrix( 0 ),
Pairwise Correlations( 1 )
);
// Create data tables of the stats required
dtCorr = Report( mult )["Correlations"][Matrix Box( 1 )] << make into data table(invisible);
dtSig = Report( mult )["Pairwise Correlations"][Table Box( 1 )] << make into data table(invisible);
// Delete the Row Name column, not wanted in analysis
dtCorr << delete columns( 1 );
// Set the 100 colors to be used to color the graph
colorMatrix = [-16558990, -16558990, -16557962, -16557962, -16557191, -16557191, -16556419, -16556419, -16555391,
-16555391, -16554619, -16554619, -16553848, -16553848, -16553076, -16553076, -16552048, -16552048, -16551277,
-16551277, -16550506, -16550506, -16549735, -16549735, -16548964, -16548964, -16547936, -16547936, -16481629,
-16481629, -16480858, -16480858, -16480087, -16480087, -16479316, -16479316, -16478288, -16478288, -16477517,
-16477517, -16476746, -16476746, -16410184, -16410184, -16343621, -16343621, -16277315, -16277315, -16210752,
-16210752, -16144190, -16144190, -16077628, -16077628, -16011321, -16011321, -15944759, -15944759, -15878196,
-15878196, -15811634, -15811634, -15745328, -15745328, -15678765, -15678765, -15612459, -15612459, -15415082,
-15415082, -15217705, -15217705, -15020328, -15020328, -14822951, -14822951, -14625829, -14625829, -14428452,
-14428452, -14231075, -14231075, -14099234, -14099234, -13901857, -13901857, -13704735, -13704735, -13507358,
-13507358, -13309981, -13309981, -13113116, -13113116, -12916508, -12916508, -12719643, -12719643, -12522778,
-12522778];
// Get the column names in the Corr data table
columnNamesList = dtCorr << get column names( string );
// Create the output
nw = New Window( "Correlations",
gb = Graph Box(
Frame Size( 800, 800 ),
X Scale( 0, N Col( dtCorr ) + 2 ),
Y Scale( 0, N Rows( dtCorr ) + 2 ),
// Loop across the the rows and columns to find the correlation values and plot the results
For( x = 1, x <= N Cols( dtCorr ), x++,
For( y = 1, y <= N Rows( dtCorr ), y++,
// Find the color to use from the color matrix
fc = Abs( Floor( (dtCorr[y, x] * 100) + .5 ) );
If( fc < 1, fc = 1 );
fcc = colorMatrix[fc];
// Set the color
Eval( Parse( "Fill Color(" || Char( fcc ) || ");" ) );
Pen Color( "black" );
// create the output square
xlist1 = Matrix( x ) || Matrix( x ) || Matrix( x + 1 ) || Matrix( x + 1 );
xlist2 = Matrix( N Cols( dtCorr ) + 1 - y ) || Matrix( N Cols( dtCorr ) + 1 - y + 1 ) ||
Matrix( N Cols( dtCorr ) + 1 - y + 1 ) || Matrix( N Cols( dtCorr ) + 1 - y );
Polygon( xlist1, xlist2 );
// Draw a line around the square
Line( xlist1, xlist2 );
)
);
// Add the "*" for significant correlations, by going through the significant data table
Text Color( "Black" );
Text Size( 20 );
For( i = 1, i <= N Rows( dtSig ), i++,
If( dtSig:Signif Prob[i] <= .05,
textList = {};
Insert Into( textList, Loc( columnNamesList, dtSig:Variable[i] )[1] + .5 );
Insert Into( textList, N Cols( dtCorr ) + 1 - Loc( columnNamesList, dtSig:by Variable[i] )[1] + .5 );
Text( Center Justified, textList, "*" );
textList = {};
Insert Into( textList, Loc( columnNamesList, dtSig:by Variable[i] )[1] + .5 );
Insert Into( textList, N Cols( dtCorr ) + 1 - Loc( columnNamesList, dtSig:Variable[i] )[1] + .5 );
// Write the symbol in the middle of the square
Text( Center Justified, textList, "*" );
)
);
)
);¿Podrías dar algo más de información para los "maniquíes de guiones" como yo?
Hace poco (hoy) hice mi primer mapa de calor en JMP siguiendo las instrucciones que se dan en este video muy útil: https://www.youtube.com/watch?v=uaBFoI3l-8U
Si alguien pudiera crear un video similar pero también incluir el guión sobre cómo agregar las estrellas (por ejemplo, * p <0.05, ** p <0.01, **** p <0.001), creo que toda la comunidad de JMP estaría realmente feliz al respecto 
Alternativamente, si alguien tuviera tiempo para explicar paso a paso qué secuencia de comandos se necesitaría y cómo podría incorporarse en un conjunto de datos JMP, aún sería muy útil.
¡Gracias por entender!