JMP Wish List

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. ... View more

Gaussian Processes are a very powerful modeling technique in predictive analytics. https://scikit-learn.org/stable/modules/gaussian_process.html   While JMP has GP, it is only for specialized modeling (not possible to fit using training/validation/test)     ... View more

Allow a user to re-open reports which took a long time to run, or reports created with random components (like bootstrapping or k fold validation), without re-running those analyses.  This way: If a user doesn't export every possible column formula from that platform, they have an option to go back to the original analysis and create them later without having to replace all previously exported columns It is possible to save the current 'session' in JMP without having started within a project. Identical analyses can be viewed by multiple users without having specified a random seed prior to running the analysis, or when using platforms that utilize multi-threading Users do not need to wait for long-running reports to re-rerun, and they can view reports that ran on much larger computers and cannot be recreated on their machine. ... View more

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 ... View more

I downloaded and ran Tarek's Model Classification Explorer Add-In that he created as a JMP Intern with @KarenC and @mia_stephens.    This is a great add-in for nominal logistic regression for holdout data so that we can apply the logistic equation to the holdout set in its own data file. It's important for a team lead on a modeling project to keep a holdout set under lock-and-key, so being able to grab it (once you think you have the best model of course!) and upload as a new JMP data file and then apply the function from our other file, and explore the performance measures, confusion matrices and ROC curves.   HOWEVER, I have had this add-in freeze on me and/or take a very long time (even with small data sets). Yes, it works on some sample data set (Diabetes, Donuts), but in the real world using real data we need to understand how to format it and input it.   * COULD YOU FORMALIZE THIS ADD-IN to function in JMP in the next version, as a platform or menu item? And maybe take some of the bugs out? And allow it to work on larger data sets? Thank you! ... View more

Currently, letter reports, indicating pairwise differences, are not generated for non-parametric pairwise comparisons such as the Steel Dwass test.    It is a pain to calculate those by hand and this has been a problem for users for years for multiple users: Can you generate a letters report after conducting the Steel-Dwass nonparameteric comparison?  ... View more

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.  ... View more

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). ... View more

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. ... View more

Hi,   I had a conversation with JMP support since I assumed a bug in JMP. However, I learned the following: - If one marks certain rows as "excluded" (no matter if chosen visible or nor) they will be removed from many computations (e.g. doing a histogram does not take excluded rows into account) - If one marks certain rows as "excluded" (no matter if chosen visible or nor) they will be included as a validation set for platforms like boosted tree or bootstrap forest. Apparently this is intended behavior. Personally, it confused me and fortunately I got in contact with support to learn about it before I published the results, since in my case the excluded rows are invalid data. I just keep those for tracking purposes.   So, finally my wish: could JMP be fully consistent in the use of excluded rows?   Here's a quote that JMP support sent me: "The Bootstrap Forest and several other platforms in JMP Pro have a feature that if some rows are excluded, and you do not otherwise specify a Validation set, those rows are used as the Validation set. To avoid those rows from being included at all, you could: 1) Subset the data table so it doesn't include those rows, then re-run the Bootstrap Forest 2) Use a different Validation method (Holdback or Validation Column) In general, it is often a good idea to devote some rows to a Validation set. This would give you the ability to use the Early Stopping option, which can help avoid overfitting."   ... View more

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.   ... View more

Please include Deming Regression for "Fit Y by X“ platform an option (check box) for a reference line y=x and the option (check box) to have x and y axis setting to be equal  Min(x_Axis) = Min(y_Axis) , Max(x_Axis) = Max(y_Axis) ... View more

Hi Please include the options for the calculation of the following for dissolution profile comparison(requirement of  regulatory agencies) a. F2 b. boot strap F2 c.Multivariate model dependent d. Multivariate model independent    This should help the people in pharma domain to use JMP more effectively.       ... View more

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).      ... View more

In the Predictive Modeling platform, it would be nice to have options, similar to the alternative confusion matrix cutoffs, to change various tuning parameters for a number of iterative steps of values. For example, in a partition, vary the minimum split size between 3 and 10 in increments of 1 (similarly for the other paramters). I realize this is not difficult to accomplish with a loop in a script, but it would be nice if it was as easy as with the confusion matrix cutoff example, or the way it works to put a variable in the By box when doing an analysis. It would then be easy to examine how various performance measures change as the tuning parameters vary. It has been my experience that most of the default parameters in JMP work quite well, but it becomes tedious to test whether or not this is true. This suggestion would automate this testing and make it easier to determine whether or not to override the defaults. ... View more

Would be nice to have more flexible column properties for spec limits and control limits. Current column properties allow only one spec limit or control limit. But many SPC users will have changing control limits and spec limits over time. Currently Control Chart Builder can use a Phase column to separate calculated control limits. Saving to a table it will show the _LCL, _Mean, _UCL, etc. for each phase, with phase as an additional column. Could spec limits and control limits work in this manner? The ability to use a table to pull in the requirements, or save from Control Chart Builder to the Column and not just the table? See wish list requests for more flexible Value Ordering and Value Labels column properties that may interact with JMP tables. Not sure how it would work with all platforms, such as Process Screening. Currently have to write scripts to add changing control limits or spec limits to charts and other platforms. ... View more

Now, Expanded Estimates are available only for basic regression models. It would be great to have them for models that are less standard, like logistic regression, ordinal regression, ... ... View more

Being an ecologist, I routinely switch back and forth between JMP and PRIMER, but, with each update of JMP, I can do more in JMP (and therefore less need for PRIMER). However, PRIMER has a multivariate, distance matrix-based algorithm called "distance-based linear modeling" (DistLM) that essentially works like stepwise regression (model selection by minimizing BIC/AICc) though with a distance matrix as the input. It is great for those looking to find the best model to explain similarity and differences between samples in a multivariate framework. I feel like JMP already has distance matrix capacity AND stepwise regression, and, although I know this is much more complicated than univariate stepwise regression, I think it should be possible to develop this technique in JMP (as well as the more commonly used PERMANOVA). Actually, it doesn't need to be DistLM, just any multivariate incarnation of stepwise regression. Pleassssssseeeeee.  ... View more

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; ... View more