JMP Wish List

TriangularCat94 · ‎11-19-2022

Moving the distribution calculator under the calculator heading rather than "Interactive Teaching Scripts" would make the categories more consistent.

TriangularCat94 · ‎11-19-2022

For teaching purposes, students often get lost in the bootstrap process in JMP. I would recommend automatically opening the Analyze Distribution box after bootstrapping the samples to help reduce cognitive load.

JMPanie · ‎11-09-2022

I would like to add an idea that allows users to export graphs with the graphlet features and embed the interactive graphs into presentations, like PowerPoint.

winfriedkoch0 · ‎11-04-2022

To visualize my wish I have used the Big Class Data Table and run Fit Model and the Prediction Profiler. Then I have done two changes for a nicer report: - Changed the label orientation of the x-axes to horizontal, - In the Effect Tests Table reduced the numbers of decimals in two columns from 4 to 2: When I now started working on the model and e.g. removed the variable age in the Effect Summary Section and entered it again then the effect of the two settings described above disappeared and the original settings appeared again. In my real working more complex scenarios apply for which the effort spent and lost is considerable. Is there a way to maintain settings e.g. of axis labels, numbers of decimals and others when developing models e.g. by adding or removing variables?

hogi · ‎10-19-2022

Tabulate cannot handle columns with modeling type "multiple response": https://community.jmp.com/t5/Discussions/tabulate-and-multiple-response/m-p/557674/highlight/true#M77131 With version 17 of jmp, Graph Builder will support culumns with modeling type "multiple response". So people will start to use this feature more and more often. The problem: Columns, for which modeling type "multiple response" will be enabled, will "disapear" in tabulate. So, there users will feel some pain when deciding which way to go: - cool new Graphbuilder features? - column still useable in tabulate alternative to "enabling support for multiple response" mode: just show the columns as if they were "nominal" mode.

Draj86 · ‎10-18-2022

Bioequivalence testing is an essential step during the development of generic drugs. Different methodologies are used depending on the requirement. For e.g, in the EU, EMA recommends using average bio equivalence test (ABE), while in the USA, FDA recommends using population bio equivalence (PBE) test. Both methods present some limitations. alternatively Bodin et. al., AAPS journal 2020 proposed method to evaluate bio equivalence: between-batch bio equivalence (BBE). It is based on the comparison between the mean difference (Reference − Test) and the Reference between-batch variability.

winfriedkoch0 · ‎10-15-2022

In ordinal logistic regression the odds ratio (under proportional hazards assumption) is the usual measure of effect size. This odds ratio is available in nominal logistic regression but not in ordinal logistic regression. It can be rather easily calculated from parameter estimates but I would appreciate that this additional step be available in JMP.

AmyBurgess · ‎10-10-2022

Following an analysis e.g. linear regression I often use the "make into data table" function. The output table changes the data from it's original format e.g. from "Numeric" and "Continuous" to "Character" and "Nominal". It also renames sometimes e.g. from "Concentration" to "Group". This means, in order to create new analyses from these tables I have to manual change the titles and data formats back to their original settings. Can we make it so that data formats and column names are retained from the original data table? JMP 16.1.0 Example: Original Column settings: Analyze > Specialized Modelling > Fit Curve Fit Curve > Polynomials > Linear Inhibitor Concentration is now “Group” Make Data Table I want to plot Slope vs Inhibitor Concentration but the column is “Group” and the Data and Modelling Type is no longer correct.

Charlie · ‎10-05-2022

I have a stacked table with a Time Point (X) column, a Cumulative Release (Y) column, and several columns of factors from a DOE. We have different specification limits for release at various time points in the dissolution curve, as required by the FDA. In development, I need to fit curves to the data, use models to determine significant factors, and optimize factors using Desirability Functions. I can fit curves with this table, but I cannot use the Desirability Functions because each column can only have one set of specifications saved with it. I can split the table by Time Point and make a new table, but that creates more documentation and review work, as required by our data integrity rules. It would be nice if there was a way to account for these specifications in Fit Model and FDE. Suggestions Allow specification limits to be set conditionally. This may help resolve the problem for non-FDE models. In FDE, allow more than one target curve. Curves based on upper spec, target, and lower spec would allow explorations of the variable space for each target curve. I use JMP Pro 16.2.0

Michelle_Ricci · ‎09-29-2022

Please consider adding an upper confidence interval for the standard deviation to the variance component analysis output in the Fit Model platform. Guidance in ICH Q14 contains acceptance criteria related to the Upper 95% confidence interval of the intermediate precision of during assay validation. Currently, the variance, 95% CI of the variance and the standard deviation (point estimate) are calculated. The variance from the VCA is used outside of JMP to manually calculate the intermediate precision value (upper CI of the standard deviation).

JMPinthePool · ‎09-22-2022

Not sure if this is already possible, but it would be really neat if there was the ability to save graph builder scripts (in the 'customize graph' dialog) in the 'templates' or 'samples' drop-down lists for easier/faster loading during future analyses.

tom_donnelly · ‎09-21-2022

Submitting this on behalf of Doug Sommerville, a JMP user at the US Army, . Doug has long used Probit analysis in his toxicology studies. JMP defaults to Logit link functions for most of this type of analysis. The Probit Link is offered in the GLM personality of the Fit Model platform. However, it only supports binary responses. Doug has a need to analyze responses with more than two ordinal levels. Allowing JMP to provide an ordered Probit analysis would greatly facilitate his work and that of anyone doing toxicology. It is common practice in this field to compare different toxic materials by their Probit slopes. It would also be great if this link function were offered in the Ordinal Response Personality as well as in GenReg. This request has come out of recent Tech Support Case Number 00025561 which contains an example data set. I have also attached the data set below. Note. Doug is a more than 20-year user of a competitor which has offered Probit analysis that JMP did not. It would be great to give him one more reason to use JMP.

sfigard · ‎09-20-2022

This is simply a labeling issue, but when teaching my biostatistics course I have to constantly deal with it. In experimental design, there is a difference between matched data and paired data. Matched data will be two different populations in which an attempt has been made to reduce the variables by matching for certain characteristics that might impact the response being studied but which aren't the focus of the study. For example, age, gender, smoking status, etc. Paired data is two populations of numbers in which the same variable has been measured on the same population usually at two different times, or under two different conditions. For example, before and after treatment with a given drug. Matched data is properly analyzed with a standard t test, whereas paired data with a paired t test. Now the problem is, the paired t test is found under Analyze > Specialized Modeling > MATCHED Pairs. This nomenclature can be confusing to students particularly when they are already having problems understanding the material (and don't particularly want to be in the class anyway, but it is required). Could this be changed to PAIRED DATA, or something other than MATCHED PAIRS? Thanks.

MRB3855 · ‎08-18-2022

Good day: The Variance Components option within the Variability Gauge plot assumes all factors are random. Often, it is not the case that all factors are random; in this mixed model scenario, it would be big step forward for this platform if some factors could be designated as "fixed" so that only the random effects would have variance components associated with them. Regards, Mark

shaira · ‎08-17-2022

In our work, we use a fixed lambda value for our analysis. Hence, our preferred platform to use for spline fitting is Bivariate platform. Fit Model has nice functionalities such as simulation profiler, data interpolation with multiple factors, etc. We also want to use fit model smoothing spline function, but it's hard to bridge with Bivariate because Bivariate Fit Spline takes lambda factor as input, while Fit Model Smoothing Spline takes number of knots as input. It would be nice to have option to set lambda instead of number of knots in Fit Model Smoothing Spline, so it can easily be bridged with Bivariate Fit Spline. Example, when I want to extend my model to 2-3 factors from my current 1-factor model in Bivariate Fit Spline. Thanks.

shaira · ‎08-17-2022

JMP's Bivariate Fit Spline has nice capabilities and customization but lacks option for addition of confidence intervals. It would be good to have a Bayesian Confidence Interval visualization whose equation we can also export as column formulas. We can have option to set the confidence interval to 95%, 90%, etc.

shaira · ‎08-17-2022

Graph Builder has hardcoded standardization X for its smoothing plots. It would be nice to have an option to turn this on or off, similar with Bivariate charts.

profjmb · ‎08-14-2022

I know there is an add-in for the "Fit Model" platform, but it's not always the easiest to use. It would be quite easy just to add Cohen's d to the output of "Fit Y by X," and this would be quite useful.

Bashburz · ‎08-08-2022

The Power and Sample Size Explorer is lacking an option for tolerance intervals. It is often desirable to get a minimum sample size for a tolerance interval based on the desired margin of error and its probability. Faulkenberry-Weeks is such an approach. Find inspiration in what Minitab does: Methods and formulas for Sample Size for Tolerance Intervals - Minitab

Florent_M · ‎07-29-2022

Hi! Currently, JMP allow to save the DTM formula, but the response is given in a array. It would be very nice if JMP propose to save the DTM formula in columns, in the same format than 'Save Document Term Matrix'. This will be very useful when a model is fitted to predict a class and you want to extend/update the database frequently in order to predict the class of the new lines. Best regards,

JMP Wish List

Move the Distribution Calculator

Automatically open Analyze Distribution after asking for bootstrap samples

Graphs with Graphlets to work in PowerPoint

Maintain settings eg in Prediction Profiler

enable "tabulate" support for columns with modeling type "multiple response"

Add Between batch bio equivalence testing proposed by Bodin et. al., AAPS journal 2020

Add an odds ratio in ordinal logistic regression analysis

Data Tables from Analysis - retain name and data attributes from previous table

Using Multiple Time Point Dissolution Specifications with Models

Standard Deviation Upper Confidence Interval for Variance Component Analysis

Save graph builder scripts as templates or samples

Ordered Probit analysis

matched versus paired data

Variability Gauge: Add a "Fixed Effect" option in the Variance Component options

Option to set lambda instead of number of knots in Fit Model Smoothing Spline

Add Bayesian Confidence Interval for Bivariate Fit Spline

Option to remove or add standardization X for Smoothing Plots in Graph Builder

Cohen's d for "Fit Y by X"

Faulkenberry-Weeks sample size calculation for tolerance intervals

Text Explorer : Save Document Term Matrix Formula in columns