cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
  • Learn how to build custom Python data connectors and further customize JMP’s Data Connector Framework with the Python Data Connector Demo, available now in the JMP Marketplace!
  • See how to create experiments to support product design and ID useful product features. Register for June 12 webinar, 2pm US Eastern Time.

Discussions

Solve problems, and share tips and tricks with other JMP users.
Choose Language Hide Translation Bar
aviquid
Level I

Value at x-axis where reference line intersects graph

Hi,

For a set of graphs, I draw a reference line on the y-axis. I want to compute what the x-axis values are at points where the reference line intersects the various curves/graphs. I have attached an image of the graph. Is there any way that I can compute these x-axis values using a script or any other way?

 

Please help. Thanks

 

aviquid_0-1685590677043.png

 

1 REPLY 1
Byron_JMP
Staff

Re: Value at x-axis where reference line intersects graph

Go to this menu:  Analyze>Specialized Modeling>Fit Curve.

 

Put in your X, Y and Group variables.

Try fitting Sigmoidal Curve, 4p Logistic, or a model like that.

 

After you fit the model, look under the red triangle menu for the fit to find inverse prediction:

Byron_JMP_0-1685610468718.png

 

 

Here's an example using the sample data of what it will look like:

Byron_JMP_1-1685610541197.png

Note: I added the reference line annotations, those don't happen automatically .

 

 

Names Default To Here( 1 );
dt = Open( "$SAMPLE_DATA/Nonlinear Examples/Bioassay.jmp" );

Fit Curve(
	Y( :Toxicity ),
	X( :log Conc ),
	Group( :Formulation ),
	Fit Logistic 4P( Custom Inverse Prediction( Response( 0.9 ) ) ),
	SendToReport(
		Dispatch( {}, "Model Comparison", OutlineBox, {Close( 1 )} ),
		Dispatch( {}, "Plot", OutlineBox, {Close( 1 )} ),
		Dispatch( {"Logistic 4P"}, "Group Summary", OutlineBox, {Close( 1 )} ),
		Dispatch(
			{"Logistic 4P", "Inverse Prediction", "Predicted Values"},
			"1",
			ScaleBox,
			{Add Ref Line( 0.3843702, "Solid", "Black", "inverse pred for std", 1 )}
		),
		Dispatch(
			{"Logistic 4P", "Inverse Prediction", "Predicted Values"},
			"2",
			ScaleBox,
			{Add Ref Line( 0.9, "Solid", "Black", "my inverse prediction", 1 )}
		)
	)
);

 

JMP Systems Engineer, Health and Life Sciences (Pharma)

Recommended Articles