JMP Wish List

According to the manual, the variable clustering colormap should be as described here: Color Map on Correlations  "The variables are arranged in the order in which they are listed in the Cluster Members table."   But unfortunately, the sort order in the map is first, by cluster, and then by variable name. The cluster members table on the other hand, is sorted by Cluster and then by RSquare with Own Cluster. This has been the case, at least since JMP 16, and it has caused some confusion, especially since axis labels are not shown on the plot, and there is no easy way to add them in the platform.   I would like that: 1. The order in the color map is as described, i.e. by cluster and by RSquare with own cluster as in the cluster members table. 2. An option to add Axis labels to the color map so that the color map can be used in presentations. The axis labels should show both the cluster number and the variable name.    It is important because currently, the plot is being misinterpreted and it is not possible to use it in a presentation without axis labels.     ... View more

What inspired this wish list request?  An interaction plot is a great tool for beginners, which also offers an overview of confounding. Interaction-plot is implemented in the tool "fit model", but newcomers will mainly use the graph menu. For a simple case of two X's, it can be easily created in the graph builder but it will be harder for more than 2 X's.      What is the improvement you would like to see?  Please, implement "interaction plot" according to the tool "fit model" within the graph menu. An option could be, that x and y could be selected and the complete 2-way interaction matrix will be created.      Why is this idea important?  It is necessary to understand interactions. There is no need to create a model to understand interactions. Until now interaction plots are hidden in "fit model" and it should be more visible especially for beginners. A specific item "interaction plot" in the graph menu would be very helpful.     ... View more

What inspired this wish list request?  There has been a shift in my field to reporting RR instead of OR. I'll be submitting a manuscript with aORs but am anticipating a review that asks for aRR instead.   What is the improvement you would like to see? Add an option in the drop-down fro Nominal Logistic Fit to perform Risk Ratios, to return results exactly like the current Odds Ratios option provides.   Why is this change important? In addition to the basic statistical reasoning for RR vs OR, I need this to publish manuscripts. ... View more

I hope JMP upgrades these features!   Python and R modules Paired: sample size calculation!         ... View more

These are things JMP cannot do: Variable vỉew windows:     Change each line in the boxplot to a different color (JMP can only change all lines in the boxplot)       ... View more

What inspired this wish list request? Please describe the current issue that needs improvement or the problem to be solved that is not easy or possible right now, with an example use case.    The idea was inspired by Figure 2.6 (seen below) from John K. Krushke's book Doing Bayesian Analysis, which displays a posterior predictive check.   What is the improvement you would like to see? Please describe the idea for improving JMP. Please include mock-ups, wireframes, screenshots, scripts, other documents or examples from other software that help describe the change you would like to see.    I'd like to have the option to display data and the prediction intervals in Fit Model and Fit Y by X platforms to generate plots such as the example below. If these options were accessible from the red triangles that would be ideal.   Why is this idea important? Please describe the value to you and/or other users if the idea is implemented (for example, ease of use, must have,…).   The posterior predictive check allows one to see if the model mimics the data with reasonable accuracy. By overlaying data and prediction intervals on the profiler, we would be able to do a non-Bayesian version of the posterior predictive check. This would help us identify differences between the model and the data, including the error term of the model.     ... View more

What inspired this wish list request? I came throught the LinkedIn post (and associated publication) from Peter Goos (Model selection for designed experiments with mixed integer programming: MIO and raster plots | LinkedIn), and I found the raster plot a very efficient visualization to help select the most relevant terms in a model.    Here is an example done on the dataset "2x3x4 Factorial" available in JMP Sample Index, where the evaluation metric can be changed (between R², RMSE, AICc and BIC) and is used to order the different models. The raster plot clearly shows where the different models agree or disagree in the inclusion of terms :   What is the improvement you would like to see? If possible in some platforms (for example in Fit Model with Stepwise personality), it would be great to have this kind of visualization to help compare and select the most relevant terms in the model(s). This visualization example can be associated with my previous wish list : https://community.jmp.com/t5/JMP-Wish-List/DoE-Fit-Model-Stepwise-Interactive-visualization-of-models/idi-p/572073 @mia_stephens this wish is similar to the previous one where we discussed about the value of visualizations for models evaluation and selection (link above).   From the Stepwise platform, I used the "All Possible Models" option to generate different models with different terms presence. The rest of the steps can be found in the following script (extracted from the log with some annotations added, sorry for the quality and length) and attached file : // Combine similar TableBoxes into a Data Table (create a datatable based on the output from All Possible Models results) // → Data Table( "Combined Data Table" ) Local( {obj}, obj = Data Table( "2x3x4 Factorial" ) << Fit Model( Y( :Y ), Effects( :X1, :X2, :X3, :X1 * :X2, :X1 * :X3, :X2 * :X3 ), Personality( "Stepwise" ), Run( Rules( "Whole Effects" ), All Possible Models( 8, 50 ) ) ); Report( obj )["All Possible Models", Table Box( 1 )] << Make Combined Data Table; obj << Close Window; ); // Text to columns Data Table( "Combined Data Table" ) << Text to Columns( columns( :Model ), Make Indicator Columns( 1 ), Delimiters( "," ) ); // Group columns Data Table( "Combined Data Table" ) << Group Columns( "X1 etc.", {:X1, :"X1*X2{y-x}"n, :"X1*X2{z-y&x}"n, :"X1*X3{B&A-C}"n, :"X1*X3{B-A}"n, :"X1*X3{D-B&A&C}"n, :"X2{y-x}"n, :"X2{y-x}*X3{B&A-C}"n, :"X2{y-x}*X3{B-A}"n, :"X2{y-x}*X3{D-B&A&C}"n, :"X2{z-y&x}"n, :"X2{z-y&x}*X3{B&A-C}"n, :"X2{z-y&x}*X3{B-A}"n, :"X2{z-y&x}*X3{D-B&A&C}"n, :"X3{B&A-C}"n, :"X3{B-A}"n, :"X3{D-B&A&C}"n} ); // Rename column group: X1 etc. → Terms Data Table( "Combined Data Table" ) << Rename Column Group( "X1 etc.", "Terms" ); // Stack data table // → Data Table( "Visualization table" ) Data Table( "Combined Data Table" ) << Stack( columns( Column Group( "Terms" ) ), Source Label Column( "Terms" ), Stacked Data Column( "Presence" ), Output Table( "Visualization table" ) ); // Change column modeling type: Presence (to facilitate the local data filter in graph builder used after) Data Table( "Visualization table" ):Presence << Set Modeling Type( "Nominal" ); // Recode column: Model ID (to have models displayed by an ID instead of long names) Local( {dt, col1}, dt = Data Table( "Visualization table" ); dt << Begin Data Update; col1 = dt << New Column( dt:Model ); col1 << Set Name( "Model ID" ); dt << Move Selected Columns( {col1}, after( dt:Model ) ); col1 << Set Formula( Recode( :Model, {Num( _rcNow ), Map Value( _rcOrig, {"X1", 1, "X1*X2{y-x}", 2, "X1*X2{y-x},X1*X3{B&A-C}", 3, "X1*X2{y-x},X1*X3{B-A}", 4, "X1*X2{y-x},X1*X3{D-B&A&C}", 5, "X1*X2{y-x},X2{y-x}*X3{B&A-C}", 6, "X1*X2{y-x},X2{y-x}*X3{B-A}", 7, "X1*X2{y-x},X2{y-x}*X3{D-B&A&C}", 8, "X1*X2{y-x},X2{z-y&x}*X3{B&A-C}", 9, "X1*X2{y-x},X2{z-y&x}*X3{B-A}", 10, "X1*X2{y-x},X2{z-y&x}*X3{D-B&A&C}", 11, "X1*X2{z-y&x}", 12, "X1*X2{z-y&x},X1*X2{y-x}", 13, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C}", 14, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X1*X3{B-A}", 15, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{B&A-C}", 16, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{B-A}", 17, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 18, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C}", 19, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{B-A}", 20, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C}", 21, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A}", 22, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{B&A-C}", 23, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{B-A}", 24, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{D-B&A&C}", 25, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{B&A-C}", 26, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{B-A}", 27, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C}", 28, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C}", 29, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C}", 30, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B-A}", 31, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 32, "X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X2{z-y&x}*X3{D-B&A&C}", 33, "X1*X2{z-y&x},X1*X2{y-x},X2{y-x}*X3{B&A-C}", 34, "X1*X2{z-y&x},X1*X2{y-x},X2{y-x}*X3{B-A}", 35, "X1*X2{z-y&x},X1*X2{y-x},X2{y-x}*X3{D-B&A&C}", 36, "X1*X2{z-y&x},X1*X2{y-x},X2{z-y&x}*X3{B&A-C}", 37, "X1*X2{z-y&x},X1*X2{y-x},X2{z-y&x}*X3{B-A}", 38, "X1*X2{z-y&x},X1*X2{y-x},X2{z-y&x}*X3{D-B&A&C}", 39, "X1*X2{z-y&x},X1*X2{y-x},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 40, "X1*X2{z-y&x},X1*X2{y-x},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{D-B&A&C}", 41, "X1*X2{z-y&x},X1*X3{B&A-C}", 42, "X1*X2{z-y&x},X1*X3{B&A-C},X1*X3{B-A}", 43, "X1*X2{z-y&x},X1*X3{B&A-C},X2{y-x}*X3{B&A-C}", 44, "X1*X2{z-y&x},X1*X3{B&A-C},X2{y-x}*X3{B-A}", 45, "X1*X2{z-y&x},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 46, "X1*X2{z-y&x},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C}", 47, "X1*X2{z-y&x},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C}", 48, "X1*X2{z-y&x},X1*X3{B-A}", 49, "X1*X2{z-y&x},X1*X3{B-A},X2{y-x}*X3{B&A-C}", 50, "X1*X2{z-y&x},X1*X3{B-A},X2{y-x}*X3{B-A}", 51, "X1*X2{z-y&x},X1*X3{B-A},X2{y-x}*X3{D-B&A&C}", 52, "X1*X2{z-y&x},X1*X3{B-A},X2{z-y&x}*X3{B&A-C}", 53, "X1*X2{z-y&x},X1*X3{B-A},X2{z-y&x}*X3{B-A}", 54, "X1*X2{z-y&x},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C}", 55, "X1*X2{z-y&x},X1*X3{D-B&A&C}", 56, "X1*X2{z-y&x},X1*X3{D-B&A&C},X1*X3{B&A-C}", 57, "X1*X2{z-y&x},X1*X3{D-B&A&C},X1*X3{B-A}", 58, "X1*X2{z-y&x},X2{y-x}*X3{B&A-C}", 59, "X1*X2{z-y&x},X2{y-x}*X3{B-A}", 60, "X1*X2{z-y&x},X2{y-x}*X3{D-B&A&C}", 61, "X1*X2{z-y&x},X2{z-y&x}*X3{B&A-C}", 62, "X1*X2{z-y&x},X2{z-y&x}*X3{B-A}", 63, "X1*X2{z-y&x},X2{z-y&x}*X3{D-B&A&C}", 64, "X1*X3{B&A-C}", 65, "X1*X3{B&A-C},X1*X3{B-A}", 66, "X1*X3{B&A-C},X2{y-x}*X3{B&A-C}", 67, "X1*X3{B&A-C},X2{y-x}*X3{B-A}", 68, "X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 69, "X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C}", 70, "X1*X3{B&A-C},X2{z-y&x}*X3{B-A}", 71, "X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C}", 72, "X1*X3{B-A}", 73, "X1*X3{D-B&A&C}", 74, "X1*X3{D-B&A&C},X1*X3{B&A-C}", 75, "X1*X3{D-B&A&C},X1*X3{B-A}", 76, "X1,X1*X2{y-x}", 77, "X1,X1*X2{z-y&x}", 78, "X1,X1*X2{z-y&x},X1*X2{y-x}", 79, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C}", 80, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X1*X3{B-A}", 81, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{B&A-C}", 82, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{B-A}", 83, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 84, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C}", 85, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{B-A}", 86, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C}", 87, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A}", 88, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{B&A-C}", 89, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{B-A}", 90, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{y-x}*X3{D-B&A&C}", 91, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{B&A-C}", 92, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{B-A}", 93, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C}", 94, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C}", 95, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C}", 96, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A}", 97, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{B&A-C}", 98, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{B&A-C},X2{y-x}*X3{B-A}", 99, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{B-A}", 100, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{D-B&A&C}", 101, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 102, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{y-x}*X3{D-B&A&C},X2{y-x}*X3{B-A}", 103, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B&A-C}", 104, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B&A-C},X2{y-x}*X3{B&A-C}", 105, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B&A-C},X2{y-x}*X3{B-A}", 106, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 107, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B&A-C},X2{z-y&x}*X3{B-A}", 108, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B-A}", 109, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B-A},X2{y-x}*X3{B&A-C}", 110, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B-A},X2{y-x}*X3{B-A}", 111, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{B-A},X2{y-x}*X3{D-B&A&C}", 112, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C}", 113, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 114, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{B-A}", 115, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{D-B&A&C}", 116, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C},X2{z-y&x}*X3{B&A-C}", 117, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X1*X3{B-A},X2{z-y&x}*X3{D-B&A&C},X2{z-y&x}*X3{B-A}", 118, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{B&A-C}", 119, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{B&A-C},X2{y-x}*X3{B-A}", 120, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{B-A}", 121, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 122, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 123, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{y-x}*X3{D-B&A&C},X2{y-x}*X3{B-A}", 124, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C}", 125, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C},X2{y-x}*X3{B&A-C}", 126, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B&A-C},X2{y-x}*X3{D-B&A&C}", 127, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B-A}", 128, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B-A},X2{y-x}*X3{B&A-C}", 129, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{B-A},X2{y-x}*X3{D-B&A&C}", 130, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C}", 131, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{B&A-C}", 132, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{B-A}", 133, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C},X2{y-x}*X3{D-B&A&C}", 134, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C},X2{z-y&x}*X3{B&A-C}", 135, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B&A-C},X2{z-y&x}*X3{D-B&A&C},X2{z-y&x}*X3{B-A}", 136, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B-A}", 137, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B-A},X2{y-x}*X3{B&A-C}", 138, "X1,X1*X2{z-y&x},X1*X2{y-x},X1*X3{D-B&A&C},X1*X3{B-A},X2{y-x}*X3{B-A}", 139, "X1,X1*X2{z-y&x},X1*X2{y… //Finally, the raster plot! Graph Builder( Size( 936, 645 ), Show Control Panel( 0 ), Variables( X( :Terms ), Y( :Model ID, Order By( :RSquare, Descending, Order Statistic( "Mean" ) ) ), Color( :RSquare ) ), Elements( Heatmap( X, Y, Legend( 10 ) ) ), Local Data Filter( Close Outline( 1 ), Add Filter( columns( :Presence ), Where( :Presence == 1 ) ) ), Column Switcher( :RSquare, {:RSquare, :RMSE, :AICc, :BIC}, Title( "Evaluation metric" ) ), SendToReport( Dispatch( {}, "400", ScaleBox, {Legend Model( 10, Properties( 0, {gradient( {Color Theme( "Stoplight Bad to Good"(1) ), Width( 12 )} )}, Item ID( "RSquare", 1 ) ) )} ), Dispatch( {}, "graph title", TextEditBox, {Set Text( "Raster plot: Model comparison and terms selection" )} ) ) ); And the datatable "Combined Data Table" contains the "Visualization datatable" on which the raster plot is created (also saved as a script if you want to have a look and change the evaluation metric). In order to reduce and optimize the computation of several models (which can be computationally intensive in presence of high number of factors, and possible terms in the models), some safeguards and model creation definitions could be implemented, in the same fashion as the design explorer for DoE, where several designs can be created in parallel. Some options to define could be the highest order of the terms to include in the models, how many different models are created, how much are displayed, ...    Why is this idea important? "All models are wrong, but some are useful" can be a great quote to illustrate the importance of this type of visualization : through the visual comparison of several models ranked by their relevance (R², information criterion, RMSE, ...to be chosen) and their included terms, any user can define which terms to include in the model with more certainty/confidence, or even choose to combine several equally good models. Multiplicity of models is often seen as a concern due to the combinatorial explosion, but this way to summarize and compare visually a lot of models could help see the models' multiplicity as an opportunity for better modeling and decision-making.   @Mark_Bailey and @jacob_rhyne, following our discussion in Discovery Summit on DoE experts expo stand :) ... View more

The "Fit Curve" platform in JMP does not support quantal (e.g. dead/alive) dose-response analysis, which is essential for bioassays with arthropods for example. Instead, users must resort to the "Fit Model" platform. While "Fit Model" can conduct the analysis using a Generalized Linear Model with binomial distribution and Logit or Probit link functions, it requires a cumbersome, manual multi-step process for essential tasks such as testing for parallelism, fitting parallel lines, and estimating potencies and relative potencies. This manual process is not only time-consuming but also susceptible to errors. In contrast, the "Fit Curve" platform offers automated procedures for similar analyses with continuous data, highlighting a gap in JMP's capabilities for quantal data. Enhancing the "Fit Curve" platform to handle quantal data would significantly improve efficiency, accuracy, and overall user experience.  Below, you will find the detailed manual steps required in Fit Model platform to analyse the results of an experiment involving three strains of insects (i.e., lab, field 1, and field 2), each subjected to five doses of an insecticide across three replicates with 20 insects per replicate.       ... View more

What inspired this wish list request? I am trying compare the Within Sigma estimate of variation between groups, and I am unable to do so since I cannot generate confidence intervals for those estimates.   What is the improvement you would like to see?  The Capability platform now helpfully includes 95% Confidence Intervals for the various Cpx and Ppx estimates, but I can’t get the same type of information for the Within Sigma estimate.  Ultimately, I would like to be able to easily obtain confidence intervals for the Overall and Within Sigma estimates in Process Capability in all platforms where that is used.  (Distribution, Control Chart Builder, Process Capability, etc.)  It would also be beneficial to generate CIs for the Stability Index to provide a quick assessment of how this ratio compares to 1.   As an editorial comment, it is harder than I would like it to be to generate the confidence interval information for the Overall Sigma Std Deviation, but I can do it in the Distribution Platform using the Confidence Intervals option.  (Would like it to be an option for the summary statistics as well).  I can also get there in Fit Y by X under the Means and Std Deviations option in JMP 17, but you have to right click the table and add the columns to the report – and not a lot of people know you can even do that.   Why is this idea important? Confidence intervals are important so that we can understand the uncertainty associated with any statistical estimate.  Without this information readily available, people will often make incorrect assumptions - especially when sample sizes are small.     ... View more

What inspired this wish list request?    Sometimes, one doesn't have access to the raw data, but rather a summary of it, commonly provided as mean and standard deviation numbers - it would be great to be able to visualise such data in graph builder, but currently the only way to plot ranges associated with an average value is to have the raw data (individual data points) so that JMP can do the stdev calculation (among others).   What is the improvement you would like to see?    Functionality in graph builder to accept a column with average values and a column with range (eg, std deviation) associated with each value.   Why is this idea important?    There must be many users who don't always have access to source/raw data? ... View more

Hello,   I would like to have the ability to set the Lilliefors curves in the normal quantile plots of Distribution to user-defined confidence intervals. Ideally, they should match the confidence interval selected by the user but at a minimum, there should be a way to change the setting independently.   Thanks and regards Mehul Shroff     ... View more

What inspired this wish list request? While using the Power Explorer platform, I find myself having to jump back and forth between different input boxes as I try and examine different operation conditions due to the fact that few of the boxes can be locked.  This makes for a frustrating and ineffective user experience.  Also, there are some one and two sample test I'd like to do power analysis for an equivalence test but there is currently no option.     What is the improvement you would like to see? Add Power to the "Solve for" drop down options Add Margin of Error to the "Solve for" drop down options Only allow what has been selected in the "Solve for" drop down  to change as the other input boxes are updated Add the ability to lock the ratio between G1 and G2 sample sizes For Sample Counts per Unit, change the "Assumed Ratio of Group Counts per Unit" inputs to a single input of the ratio of group 1 to group 2 defect counts per unit. Add in one and two sample Equivalence test for Proportions and Counts     Why is this idea important? The Power Explorer platform is a key platform but is hard to use and less intuitive than the Minitab equivalent(though it does have more and better capability!).   Thanks for considering this! Steve   ... View more

What inspired this wish list request?  Preview for functions under the Tables menu is very useful to check the correct manipulations have been performed.  However, the generation of the preview table can take considerable time if the data tables involved are large.   What is the improvement you would like to see?  Preference setting to allow for the table preview and table preview auto refresh to be disabled by default.   Why is this idea important?  Saves processing time for the user.   ... View more

What inspired this wish list request?  In my research I often have to compare different experimental conditions for their proportions (mortality for instance). I can perform a chi square test with the Y by X plateform with the Treatments in the X, the parameter in the Y (living vs dead) and the count as Freq. I obtain a global p-value but then I can't discriminate treatments.   What is the improvement you would like to see?  I would like to have a post hoc procedure to hava a multiple two-samples proportions test in order to test all pairs along with a connecting letters report. Ideally a correction for multiplicity (as Tukey) would be needed. I've been using another stat tool with the Marascuilo procedure.   Why is this idea important?  It will be important for all situations where there are more than two groups to compare for their proportions   ... View more

What inspired this wish list request? I would like using the label orientation "Automatic" consistently but currently I must define it at each individual axis in graphs.     What is the improvement you would like to see? I would like to have it as a preference so that it is applied automatically to all my graphs.     Why is this idea important?  The proposal is important because it saves us a öot of clicks.     ... View more

Qu'est-ce qui a inspiré cette demande de liste de souhaits ?   La possibilité de le faire sur Minitab / Excel Le fait qu'un biais n'est pas forcément centré sur 0 exemple : [-1 ; +2 ] Que le calcul de l'intervalle de confiance du biais fait intervenir aussi bien l'Upper Limit que la Lower Limit du biais et pas forcément le delta calculé.   Quelle est l’amélioration que vous souhaiteriez voir ?   Pouvoir avoir la possibilité de saisir les deux limites et non que le delta comme c'est le cas actuellement.   Pourquoi cette idée est-elle importante ?   Indispensable pour la gestion des tests d'équivalence dont le biais n'est pas centré sur 0, aujourd'hui nous devons utiliser Excel pour mitiger cet écart (JMP pas utilisable donc).     ... View more

What inspired this wish list request? I love the Difference vs Mean plot in this platform, but many times the difference may be a function of the mean. What next?     What is the improvement you would like to see?  I would like to be able to fit a line of Difference vs mean and see if it is significant, better yet, have JMP do it and suggest a different analysis like % Difference vs Mean (Available in JMP 18), and see if this one has a trend also. If it does, suggest another approach. At a minimum provide a way to output the data used in the plots to a data table so I can check myself. I think the Galton.jmp sample data file is a good example where people will get the incorrect conclusions. The Matched pairs analyses leads one to believe the difference is not significant, when in fact some assumptions of the paired t-test are violated, and this is not the correct conclusion. The assumption that the difference is random about an average difference is not true in this case.      Why is this idea important?  This will lead JMP users to the correct analysis in cases where the paired t-test assumptions are violated.     ... View more

What inspired this wish list request?    Whenever i move/organise my files and folders, all of the JMP analysis links break if the graph has used a reference to the data table rather than embedding - this is most annoying/inconvenient, and means that i have to perform the analysis all over again. I believe this is caused because the link between the analysis file and the data table file is an absolute reference (ie, starting at C:/)   What is the improvement you would like to see?    It would be SOOO helpful, if those 'absolute' references could be replaced by relative references - since quite often, my analysis files are stored in the same folder as the data table that they reference. Doing this would mean i could then put the files wherever i like and they would still work and be useful.    Why is this idea important?    This would make it much easier for your users to organise their work after performing the analysis of data, rather than having to think completely proactively about how many files you might generate, and whether it might be better for them to have their own folder/location on a drive.    ... View more

Hello,   I often generate multiple Bivariate plots from a dataset using a By variable. If I change the axis type (e.g., from Linear to Log) on one plot, I can then broadcast it to the other plots. However, when I do this, the scale range for the first plot also gets broadcast. I also don't want to set a one-size-fits-all scale. Can this platform be enhanced to allow interactively changing the axis type while still auto-scaling each plot based on its data? I know there are ways to do this with JSL but the specific request is to be able to do this interactively.   Thanks and regards Mehul Shroff ... View more