JMP Wish List

I think it should be possible to add custom axis breaks to the Graph Builder, in the same way it is often possible in other graphing software like Origin Lab.   At the moment, producing a good display for continuous X-axis data with X-axis grouping is quite involved. For example: if we have continuous timestamps over multiple days, but the data goes only for one hour during each day, but you still want the graph to show the course during a single day.   Previous post explaining the problem: https://community.jmp.com/t5/Discussions/How-to-insert-X-axis-break-in-graph-builder/m-p/525673 ... View more

JMP long ago wrote a White Paper on the procedural steps to go thru to produce interpolations (at the X's I DESIRE/NEED) from a spline fit.  Works fine.  As with Excel files, you keep opening and et cetera, so you don't have to create from scratch the formulas and so on.  Every time though it's still a bit tedious.  The rows adding / the columns creation / whole new data table too and so on.  So even with an already done set of formulas, still tedious.  I'd like to see JMP have a formula or "red triangle" that says "USE THE SPLINE TO INTERPOLATE / ESTIMATE VALUES BASED ON THE SPLINE FOR THE VALUES IN THIS ______ COLUMN" and the red triangle would have a dialog to simply ask you for which column of X's are the spline "responses" to be interpolated.  Once JMP has LOESS regression (per my suggestion), DITTO there.  LOESS interpolate with a red triangle to pop up the dialog, for which column of X values do I want LOESS-Interpolated response estimates.  For the motivation, see my LOESS suggestion.  It's in the realm of metrological statistics.  Fully rigorous, but not fancy for the sake of it.  And a FULLY PRACTICAL NEED.  A NEED unfulfilled (the LOESS) and fulfilled only with a big rigamaroll (interpolation of spline via the White Paper involved procedures). ... View more

Let's say you are running a One-way ANOVA with 5 groups.  You test for equal variance and conclude that the assumption of equal variance does not hold.  You then evaluate Welch's test.  The p-value from Welch's test indicates that the 5 group means are not equal.  Is there a simple way to run a multiple comparison test (to compare the 5 means or a Dunnett's like test to compare 4 of the means to a control group) when you have unequal variance?    Ideally, this would be an option found in the "red triangle" when using Fit Y by X (for the One-way ANOVA case).  Perhaps found in the Compare Means choices (see attached for a possible example).   Thanks ... View more

Hi, I think it would be a terrific idea to enable the option to turn on sample size (n) within Least Square Means Tables within the Fit Model Platform, particularly for cases of retrospective data analysis where sample sizes are often unbalanced across groups.  The reason being is that the platform frustratingly shows you the total sample size for the model, but doesn't show you how many samples belong to each respective variable you are testing.     Also, while the LS Means Plots are an excellent visualization tool for comparing the LS means by group, they can be somewhat misleading to the statistically naive because they don't illustrate where the individual data values are located in relation to the mean location and LS mean error bars.  For this I would recommend allowing the user to turn on the points as an overlay to the current Least Square Means Plot.     ... View more

I'd like to have a multivariate x axis in the Fit Y by X platform, with the ability to run Means testing. I use the Variability chart in the variability gauge platform to show how multiple (non-continuous) variables grouped on the X axis impact a Y numeric product response. I'd like to be able to run means ANOVA testing for each of the variables plotted on the X-Axis.  ... View more

I was recently running OC curves to determine AQL and RQL figures. Of interest was the difference in these figures when different sample sizes are used. It would be great to have the option of entering multiple sample sizes (along with population size, and allowable defects), and then plot the overlapped OC curves on one plot to help you visualise the ‘trade-off’ that exists in AQL/RQL figures for various sample size options. At present, JMP will only show curves for individual sample sizes. ... View more

The tolerance intervals in the distribution platform require you to adjust the proportion and confidence levels for each response individually.  It would be great if a preference could set the confidence, proportion and 1-sided/two-sided along with the option to remove the input prompt when performing the analysis. ... View more

Don't make me get SPSS - include an ANOVA with a correction for unequal variances (not to test IF there are unequal variances but to actually run the ANOVA when they ARE unequal variances) ... View more

Dear members of the JMP Team,   May I suggest to implement the QUADE test in the portfolio of the statsitical tests offered by JMP: This test is especially heading at low or very low sample sizes (down to n = 5).   Many thanks for condering my suggestion.   Happy Easter,   Ulrich Schiefer ... View more

Extend Fit Ordinal Logistic by the following two features: - Add the Odds Ratios together with 95%-CIs, - Add a test to check the proportional odds assumption. ... View more

It would be nice if Tolerance Intervals calculated in the Distribution Platform came with a visualization (e.g.) on top of the Histogram. The graph below gives an example. There, I added the Graph Builder visualization of a Tolerance Interval below the Distribution Histogram. I left out Spec-Limits there, but if so specified in the column properties, they should be shown there, as well. Similar to other features in Distribution I image it would be possible to show or hide this visualization via the red triangle menu.   There are further ideas presented in the Wish List in connection with Tolerance Intervals, e.g. https://community.jmp.com/t5/JMP-Wish-List/Setting-Confidence-and-Proportion-to-Cover-for-Tolerance/idi-p/51188 https://community.jmp.com/t5/JMP-Wish-List/3-Sigma-Intervals-and-Tolerance-Intervals-in-Graph-Builder/idi-p/51192 https://community.jmp.com/t5/JMP-Wish-List/Statistical-rationales-for-sample-sizes/idi-p/52052   ... View more

See the attached journal.  This is a simple example of a variability/gauge plot. from one of the sample datasets. I do a lot of beginning JMP user training, and I always show them the variability chart because it is so useful for visulaizing the effects of different X factors on a Y response all on one plot. I would like to be able to do more statistical analysis off this platform. I am looking for the same analysis that is on the fit Y by X platform. For instance in this case an Anova/Tukey means comparison by Gender. And/or a T test comparing gender by each drug. ... View more

Other places where you are picking a column have a little red triangle (lrt) that allows me to select things that "contains" or such.   When I put ~100 (ish) columns in, and want to reduce them in the effect summary, it would be so much nicer if I could limit the shown columns to those containing or not containing some piece of text in the name. ... View more

As a JMP Partner in Brazil, I teach people how to use JMP to solve problems using data, and I’ve received some feedback on the language options available within JMP. While multiple languages are supported, Portuguese is not yet one of them. Including Portuguese would be a significant step toward expanding the JMP community in Latin America. Brazil is a huge market with tremendous potential! Although there are thousands of JMP users here, not all are fluent in English or Spanish. Our competitors’ software includes Portuguese support, which often makes it the preferred choice for some users. Adding a Portuguese option would greatly benefit the JMP community and increase adoption across Brazil. Thanks! ... View more

What inspired this wish list request?  JMP is super useful in the bioassay world. However it is not easy to constrain asymptotes especially when plotting multiple curves from the same plate. Also using applying the 4PL fit curve in graph builder is not easy. it should be available just like linear regression. This would really make your program powerful.    What is the improvement you would like to see? Please add 4P Hill fitting equation to your Graph Builder platform, just like thelinear regression and other options you have. It would just be a button icon as an option. Even more powerful would be the option to constrain the asymptotes within graph builder. The constraint option in non linear graphing is very cumbersome and often does not give the same results as GraphPad Prism which is the gold std for graphs in the biologic community. JMP is so much more powerful because of the way you can easily look at data and change axes etc, this would make your program superior to GraphPad and many more users would come to you if this was an option.   Why is this idea important? 4P Fitting is extremely important in many biological assays. If you want to extend the reach of your program making this easy will do that. We constantly have to toggle between graph pad Prism. Prism is not ideal because data import is very cumbersome so we end up having to import data with JMP and then copy paste into Prism.      ... View more

What inspired this wish list request?  During a webinar this morning by JMP about predictive modeling for collinear and high-dimensionnal data, when using Fit Model platform and trying to save model column formula, it was a bit harder to determine which columns were leading to a formula and which ones were only saved values. In the Fit Model platform, the number of saved columns options can quickly become very large, example : In some platforms, it may not be very clear if the saved columns will contain formula (for example probabilities for SVM in the case of classification).    What is the improvement you would like to see?  In JMP, there is already a visual way to differentiate column values from column formula through the little "+" icon in the data table. By using and extending this icon to other platforms that could enable to save and export formulas, it would better highlight which saved columns contain a formula : Using and extending this icon/design would better facilitate the expectations and lead to quicker actions.   Why is this idea important?  By this simple change, it could provide a more uniform user experience and better homogeneity in design interfaces. ... View more

What inspired this wish list request? The right-click bootstrap feature is not currently available in reports that use by-group processing. If one wants to run bootstraps on multiple subsets of data, rows in the data table must be manually included/excluded and run as separate distributions.   What is the improvement you would like to see? The right-click bootstrap feature becoming available in reports that use by-group processing would be a major time saver and help reduce manual filtering errors when subsetting the data for each group's bootstrap.     Why is this idea important?  Bootstrapping is a tool my colleagues and I use regularly while writing reports. It would save a lot of rework and, again, truly reduce manual filtering errors when subsetting the data for each group's bootstrap.     ... View more

What inspired this wish list request?  When I create a 2X2X2 factorial design with 2 replicates (16 total runs), and run the reduced model with factor effects A, C, and AC, I expect to get the lack of fit test outcomes because in this case, I have 8 pure error degrees of freedom and remaining 4 degrees of freedom can be used for lack of fit test. But when I really run the model, JMP doesn’t activate the lack of fit report. This is because JMP completely ignores a column B and recognize the model with A, C, and AC as a full model in two factors A and C. But it is truly not a full model in terms of three factors A, B, and C.     What is the improvement you would like to see?    I suggest let JMP keep the information of factors involved in the data (not the model) and use it to provide a LoF report.   Why is this idea important?  For example, suppose a user built an initial model with all possible terms in 3 factors factorial design. By investigating the effect summary window, the user can remove some unnecessary terms one by one. Whenever a term is removed, the user may want to confirm the elimination by checking the LoF report. However, currently, if all the terms involving a specific factor are eliminated, LoF is not provided. This is probably not only restricted to 2-level experimental data; the same thing might happen with any general datasets, I think.       ... View more

What inspired this wish list request?  Using noncompartmental analysis, (NCA), one can derive the pharmacokinetic (PK) parameters of a drug by looking at the measured drug concentrations over the course of time. Because of the nature of this approach, the assumption of a particular compartmental model is not necessary. NCA is frequently utilised to determine the degree of exposure that occurs after the administration of a drug, such as the AUC, as well as other pharmacokinetic parameters, such as the clearance and the terminal half-life. NCA curve fits are favoured by the regulators and are an essential part of the bioavailability / bioequivalence (BA/BE) studies. The present Fit Curve > Pharmacokinetic models only support oral and two compartment IV Bolus. Hence, the introduction of a NCA PK curve fit for oral and IV infusion and bolus would be highly beneficial from a regulatory standpoint.     What is the improvement you would like to see?  Generic Drug industry is a mainstay for pharmaceutical businesses and is valued at ($ 255 Bn ( 2021 est.). Equivalence trials are typically conducted using a crossover design, but a parallel design can also be used. Bioequivalence trials compare two drugs. The FDA believes that if two drugs have similar absorption properties, they will have similar effects and safety profiles. An equivalence trial evaluates a generic drug's equivalence to an existing drug. This is done by comparing a confidence interval about the difference between two drugs' pharmacokinetic measurements with a U.S. federal confidence limit. Bioequivalence is declared if the confidence interval is within the limit. Test interval hypotheses to assess bioequivalence. Several pharmacokinetic measures determine a drug's bioavailability. AUC is the most common measure (AUC).Maximum concentration (Cmax) is another measure of drug availability and its associated PK measures. Also, a statistical summary of the NCA PK fits helps to understand the drug disposition profile across subjects of the trial.     Why is this idea important?  Pharmaceutical companies and CROs currently use SAS and other nonlinear curve fit tools to do the NCA and having this feature inbuilt into JMP completes the development spectrum of the drug product with JMP. Since a pharmacokinetic curve fit already exits in JMP and extending its capability will entice more customers and would complete the entirety of this capability.     ... View more