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JMP Wish List
We want to hear your ideas for improving JMP. Share them here.We want to hear your ideas for improving JMP software
We know that you have many valuable ideas on potential new features, or enhancements to existing features. You can use this Wish List to share your ideas, and discuss how to improve JMP, with other users across the globe.
Here's how to participate:
- Search: Please search for an existing idea first before submitting a new idea.
- Kudo & Comment Kudo ideas you like, and comment to add to an idea.
- Subscribe: Follow the status of ideas you like. Refer to status definitions to understand where an idea is in its lifecycle. (You are automatically subscribed to ideas you've submitted or commented on.)
- Submit: Post your new idea using the Suggest an Idea button. Please submit one actionable idea per post rather than a single post with multiple ideas.
We can’t implement everything, so please include the following to help JMP Product Management evaluate your ideas for consideration:
- 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.
- 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.
- 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,…).
Showing ideas with label Predictive Modeling and Machine Learning.
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I know it is possible to do bootstrapped CI in JMP. But, when we build and test complicated mediation model, the indirect effects need to be calculated and then a bootstrapped CI is needed to build on that indirect effect. I am sure some smart JMP user has figured it out. I hope that person may write a script or a step-by-step procedure for doing this in JMP. Otherwise JMP developers, please take a note of this wish list.
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Automation and Scripting
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Predictive Modeling and Machine Learning
Status:
Archived
Submitted on
12-11-2017
03:20 PM
Submitted by
MarkAldonWeiss
on
12-11-2017
03:20 PM
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).
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Advanced Statistical Modeling
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Basic Data Analysis and Modeling
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Predictive Modeling and Machine Learning
Really would like more than the current hidden layers available. JMP Pro makes analysis very easy for CART, Random Forests, Naive Bayes, and SVMs but the Neural Net model is limited to too few layers. Consequently I go to R to build more sophisticated deep nets. Would be so much easier if this capability was incorporated into JMP. Thanks, Shaun
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Predictive Modeling and Machine Learning
In continuation of this exchange: Interactions in OLS regression and simple slopes.
I would like to propose that JMP in a similar manner to SPSS Process or simple R scripts would include in multiple regressions that calculate interactions between variables, the independent value of each variable slope and not only their additive value.
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Predictive Modeling and Machine Learning
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.
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Advanced Statistical Modeling
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Data Exploration and Visualization
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Predictive Modeling and Machine Learning
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Sharing and Communicating Results
Since JMP emphasizes visualization, the augmented factor correlation matrix with color coding will be a useful piece of information in the SEM platform. The factor correlation matrix is a useful for researchers during scale construction. For multi-item constructs scale builders look for convergent and discriminant validity. This piece of information can be obtained easily since JMP 15 can now compute factor scores for latent variables. This correlation matrix can be augmented as follows:
1. Correlation values on the lower diagonal
2. Correlation-squared values reflecting shared variance between factors on the upper diagonal
3. Average Variance Extracted (AVE) which is the variance each factor shares with its own indicators on the diagonals. This can also be thought of as the average communality of the construct.
Here is an example:
For convergent validity, we want AVE > 0.5 indicating the construct explains > 50% of the variance of its indicators For discriminant validity, we want the diagonal values to be higher than values to the right or above since this establishes that the factor shares more variance with its own indicators than other factors. When such is not the case, color coding the violations will be more useful.
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Consumer and Market Research
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Predictive Modeling and Machine Learning
- Find more ideas tagged with:
- JMP 16
1. Multiple-comparisons for nominal Y's (as for continuous Y's in Fit Y by X and in Fit Model).
2. Multi-way contingency tables for factorial layout of X's, and a nominal Y.
3. Contingency table: give proper titles to the last column and last row (e.g. "Y sub-total" and "X sub-total").
4. Contingency table: when "Col%" is requested, it should be presented also in the "Y sub-total" column and when "Row%" is requested, it should be presented in the row of "X sub-total".
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Data Exploration and Visualization
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Design of Experiments
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Mass Customization
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Predictive Modeling and Machine Learning
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!
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Predictive Modeling and Machine Learning
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.
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Predictive Modeling and Machine Learning
I suggest that the output for regression include options for the Johnson-Neyman regions of significance—i.e., floodlight analysis (i.e., at and above/below what value of the moderator is the difference between levels of the independent variable significant). In addition, I suggest including options for spotlight ananyses, based on regions defined by the user (e.g., significance at +-1 SD from the mean of the moderator or at a particular value of the moderator).
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Predictive Modeling and Machine Learning
Status:
Archived
Submitted on
05-10-2018
06:23 PM
Submitted by
uday_guntupalli
on
05-10-2018
06:23 PM
I would like to propose the ability to have scriptable control for code folding markers. This in my opinion would enable easier management of code. For e.g. SelectableMatrix =
Expr(
ResMat = {};
// loop through each row
For( i = 1, i <= nR, i++,
ResMat[i] = {};
// loop through each column getting values
For( r = 1, r <= nC, r++,
ResMat[i][r] = TB1[CheckBoxBox( r )] << get( i )
);
);
ResMat = Matrix( ResMat );
); The ability to then use actions like the following to contract and expand the code will make it easier to manage the code. SelectableMatrix << expand;
SelectableMatrix << contract;
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Mass Customization
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Predictive Modeling and Machine Learning
I would like to see the RSquare validation value available for each row in the output table with the all possible models tool. I find myself using the all possible models option often but I always end up needing to manaually create the exact table to include the validation information. The information is already avaialbe without actually running the model in the stepwise regression control on the top of the page.
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Advanced Statistical Modeling
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Predictive Modeling and Machine Learning
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).
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Predictive Modeling and Machine Learning
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.
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Predictive Modeling and Machine Learning
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.
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Content Organization
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Data Access
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Mass Customization
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Predictive Modeling and Machine Learning
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!
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Status:
Acknowledged
Submitted on
10-18-2024
08:53 AM
Submitted by
Victor_G
on
10-18-2024
08:53 AM
Victor_G
on
10-18-2024
08:53 AM
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.
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Basic Data Analysis and Modeling
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Predictive Modeling and Machine Learning
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Sharing and Communicating Results
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
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Predictive Modeling and Machine Learning
Status:
Acknowledged
Submitted on
10-05-2024
08:50 AM
Submitted by
utkcito-GMC
on
10-05-2024
08:50 AM
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.
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Advanced Statistical Modeling
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Basic Data Analysis and Modeling
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Data Exploration and Visualization
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Predictive Modeling and Machine Learning
Status:
Yes, Stay Tuned!
Submitted on
10-02-2024
06:48 AM
Submitted by
jthi
on
10-02-2024
06:48 AM
jthi
on
10-02-2024
06:48 AM
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.
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See more ideas labeled with:
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Content Organization
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Data Access
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Predictive Modeling and Machine Learning
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Sharing and Communicating Results
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Data Exploration and Visualization
551 -
Automation and Scripting
441 -
Mass Customization
325 -
Data Access
236 -
Basic Data Analysis and Modeling
233 -
Data Blending and Cleanup
222 -
Content Organization
178 -
Sharing and Communicating Results
166 -
Advanced Statistical Modeling
141 -
Quality and Process Engineering
117 -
Predictive Modeling and Machine Learning
105 -
Design of Experiments
90 -
Reliability Analysis
60 -
Consumer and Market Research
42
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