JMP Wish List

aharding · ‎11-02-2023

What inspired this wish list request? In comparing models there are some more statistics and graphs that may help with this comparison. What is the improvement you would like to see? Statistics to consider adding are R-squared adjusted and R-squared predicted, AICc and BIC. Would like to see a table listing all of these beside one another along with the coefficients and p-values for each term in the models. Potential graphs that may be helpful are Absolute value of the residual vs run for each model and Absolute value of the residual vs model (like like A, B, C) to show how the average Absolute error compares and the variation. Why is this idea important? This will strengthen the ability to compare the models quickly and better enable the more correct model to be chosen.

jszarka · ‎10-09-2023

I don't have a lot of faith in the 'plot residuals by normal quantiles' for doing model diagnostics. I have typically saved the residuals from the model to a new column, then used the Distribution platform to create a normal quantile plot there. The fit of the solid red line does not seem to match up well with where the residuals fall using Fit Least Squares. The slope of the solid red line is not a good fit. I looked at a few sample data sets and saw issues with the default model scripts in these data sets: 2x3x4 factorial Bounce data Wine data Below is a plot of the 2x3x4 factorial Residual Normal Quantile Plot. I've been told that the slope of the solid red line is based on the RMSE of 2.02. If I save the residuals to a new column and regenerate the Normal Quantile Plot from the Distribution plot, the line is based on the standard deviation of the Residual Y column of 1.03. It looks like a much more sensible fit from a model diagnostics perspective. Is this something that can be updated in future versions of JMP? Note that when you use Fit Mixed, the results in the Residual Plots there match my desired result from manually saving the residuals to a new column.

andrewtkarl · ‎06-27-2023

What inspired this wish list request? The SVEM prediction columns are currently being saved with an ad hoc correction applied to the the vector of predictions by fitting a simple linear regression of the SVEM predictions to the original prediction column and then saving the predicted values from this simple linear regression. What is the improvement you would like to see? Even if it is a hidden or scripting-only option, I think it would be nice to have an option to disable this correction and simply save the raw SVEM predictions. Why is this idea important? I would use this for testing the properties of the predictions with and without the correction. I have talked to some other users who would also be interested in this ability for the purposes of testing SVEM.

paulp · ‎06-07-2023

What inspired this wish list request? SHAP values are invaluable for explaining ML models. What is the improvement you would like to see? The inventor of SHAP values, Scott Lundberg, has a paper Consistent Individualized Feature Attribution for Tree Ensembles. It says "SHAP interaction values can be interpreted as the difference between the A. SHAP values for feature i when feature j is present and B. the SHAP values for feature i when feature j is absent." Why is this idea important? My experience is that prior to modeling, a majority of collaborators ask if interactions between factors can be investigated. Explainable ML is the key to model acceptance, and obviously ML models are deployed where nonlinearity is suspected to dominate (otherwise regression would be sufficient).

Raaed · ‎05-30-2023

Hi everyone I suggest for jmp staff create add-ins JMPGPT - JMP® app and class to access ChatGPT API from OpenAI™, such that JMPGPT allows you to easily access the ChatGPT API provided by OpenAI. With this add-in, maybe you can load a list of prompts for specific use cases and engage in conversations with ease. If you're new to ChatGPT and prompt engineering, the JMPGPT add-in will be a great way to learn. Raaed ~✍

d_barnett · ‎05-30-2023

With the increase in speed of deployment and usefulness of AI tools like ChatGPT could JMP use this as part of EasyDoE and other platforms to help users design an experiment as well as the analysis Using a simple chat window linked to the AI it could ask you questions about the factors, constraints etc and this could then use JMP to build up the design of the experiment. Other companies are taking this approach to AI. AI seems to be the future and integrating it into tools like JMP will be essential for the growth of data analytics in the (very near) future. Not implementing this soon could mean users look elsewhere for the simplicity of interactions with AI.

John89 · ‎01-29-2023

What inspired this wish list request? Past personal need and experience with colleagues who are implementing partial signal analysis algorithms to analyze data. What is the improvement you would like to see? Signal processing functions for standard results found in pythons scipy, including filters(digital & analog), FFT (spectrum), noise, convolution, correlations, transformations, peak analysis, SNR, THD, and vibration analysis. If possible I would go further and add functions for simulating signals as well, this would also be great for teaching and well as synthesizing signals for specific needs with good control and intuitively. Why is this idea important? I believe one of JMP's major strengths is the low entry level that it allows of it's users to be able to do complex tasks quickly while assure it's integrity and also assist users in gaining a deep understanding of processes. I feel the area of signal processing lacks this immensely and feel that the folks at JMP can contribute greatly to this field. This is a major field with increasing traction and I believe this would align well with JMP's renewed emphasis for targeting academia.

Reinaldo · ‎05-03-2018

Hi JMP users, I used to run some non-parametric comparisons for each pair using the Wilcoxon method in a repeated-measures designs through Analysis -> Fit Y by X report. I would like to know if it is possible to develop an effect-size calculator (add-in) for that purpose, please. Thank you.

ih · ‎09-26-2022

Provide a way for users to define their own model and optimization techniques in the model screening platform, including tools from R or Python, enabling almost any model to be called by this platform, including techniques that have not yet been added to JMP, or perhaps never will be. This could be similar to defining custom JSL functions where the user would define a function for performing the analysis and returning summary statistics and model parameters, and then another function to launch the analysis based on those model parameters returned from the initial optimization. This could be all neatly wrapped in an Associative Array along with names and descriptions. I expect this would most commonly be managed as part of an Add-in, if needed I think it could always be managed that way. The custom functions should allow users to call R or Python within their function. In that case the function they define to launch the analysis might need to launch a new window they created to interface with R or Python. I briefly hoped this was already possible as the XGBoost Add-In for JMP Pro add-in appears to add a new model to the platform, but it sounds like this is a special case that is hard coded in JMP itself.

Lu · ‎08-23-2022

In medical research the outcome under study is frequently rare, so the data are highly unbalanced. The model perfomance can significnatly improofed by correction for inbalancing by over- or undersampling. Therefore, I would suggets that all prediction model platforms should have the inbalanced classification option. Even better, the Model screening platform should have the inbalanced classification option included. Regards, Lu

fever · ‎10-26-2021

The addition of quantile regression to the generalized regression platform was/is very welcome. It would also be nice if pseudo rsquare and other goodness of fit measures for quantile regression could be provided in the results. Thanks.

VincentBechard · ‎04-16-2021

Since JMP 16, either the normal and pro versions, the menu option for cross-validation is gone! To use cross-validation with decision trees, you need to use the Model Screening platform (new in JMP Pro only), and you can't modify any tree building option in there. This is more a downgrade than an upgrade! It was very convenient to have cross-validation along with all other options in a self-contained analysis platform.... Please bring it back!

dmkMozart1 · ‎04-16-2021

According to a Stack Overflow post, the maximum line length in SQL Server Management Studio is "65536*Network Packet Size - which by default is around 1500 bytes." When recently exporting SQL scoring code through the Formula Depot in JMP Pro for a Bootstrap Forest model the code was formatted as only three lines of incredible length. SQL Server couldn't even parse the code and simply paging down in the code became a time consuming process. When copied into Word, the document was >2,000 pages long! This could be avoided by either A) fully parsing the code with line breaks and indentation using common best practices or B) adding line breaks every so often into the generated code at a few logical points such as at the start of each new CASE statement with a rule to force in a break before the limit is reached.

Narayanan · ‎12-16-2019

Can we have tables of indirect effects and total effects in Structural Equation Models? When there are mediating effects in the model, the direct regression effects table gives only a partial picture of the full model. When the indirect effect has an opposite or nullifying effect, then the total effect coefficients give the full picture of the entire model. Bollen (1989) gives formulas for the total effects of latent variables on other latent variables and observed variables which might be useful in doing prediction (wishful thinking).

ghartel · ‎06-17-2019

Predictive models with binary Y that allow cross validation, eg k-fold or leave one out, the confusion matrix reflect only the last iteration of the cross classification. For example in 5-fold cross validation the training set has 80% of the data and the Validation set shows 20%. The validation set should combine all 5 folds of the data to show 100%. For leave one out it is even more useless as the validation set only has a single observation.

Matheus_Plana · ‎10-25-2024

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!

Victor_G · ‎10-18-2024

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.

fcas80 · ‎10-09-2024

What inspired this wish list request: JMP seems to have a measure for k-means to choose an optimal number of clusters. What is the improvement you would like to see: I believe hierarchical clustering has some statistical measures such as silhouette to statistical choose an optimal number of clusters. If this does not currently exist within JMP, perhaps you will consider adding it. Why is this idea important? A statistical measure would be more accurate than the current graphical technique

utkcito-GMC · ‎10-05-2024

Hello, I am a physician researcher. In biology and clinical measurements, many times it is difficult to analyze data numerically because of patient heterogeneity, no real baseline, etc. What we do have is trends, i.e. "where is this (value) going". Biologically, many times the changes, trends and trajectories are more important than absolute values, or just more reliable. Therefore when I'm following up patients or results from biological samples, it is not uncommon for me to need to see what is the trend over time of a group of patients or samples. Then I want to compare them, cluster them, etc. There are some techniques to do that, very well reviewed and exemplified here: https://doi.org/10.1093/bib/bbad390 Another important aspect of this (local similarity analysis) is the ability to synchronize signals as they evolve over time. Having these features in JMP would be invaluable! Uriel.

jthi · ‎10-02-2024

What inspired this wish list request? Documenting your models and data can be difficult but it is becoming more and more important. What is the improvement you would like to see? Some time ago I came across Model Cards (Google Model Cards, [1810.03993] Model Cards for Model Reporting (arxiv.org)). Something similar could be implemented in JMP and I would also implement it for datatables. Easy platform which can be utilized to define specific things related to your datatable/model (description/details, intended use,...). In best case this could be printed in some format which could then be utilized outside of JMP. Why is this idea important? It is important to document your models and data but it can be a hassle. Currently (in my opinion) it is very difficult to do in JMP in any sort of efficient manner which would be easy to access, fill, update and read later.

JMP Wish List

Model Comparison Platform needs more things added.

Fit Least Squares - Plot Residuals by Normal Quantiles Improvements

Option to disable ad hoc adjustment applied to SVEM predictions

SHAP interaction values

JMPGPT

Use of Artificial Intelligence For Design And Analysis Of Experiments

Signal processing module/Toolbox

Effect-size calculator (add-in) for nonparametric comparisons (Wilcoxon) repeated-measures designs

Support custom models in Model Screening

inbalanced classification option

pseudo rsquare and other goodness of fit measures for quantile regression

Bring back the crossvalidation option in Partition platform menu

Limit generated SQL code line length to maximum allowed

Total Effects and Indirect Effects Computation for General Structural Equation Models

cross validation confusion matrix should include all cross validated data not just last

JMP in Portuguese!

Identify formula column in model platform

Hierarchical clustering optimal number of clusters

non-parametric trends

Implement Data and Model cards for documenting models and data tables