Predictive and Specialized Modeling > Response Screening > Launch the Response Screening Platform
Publication date: 04/12/2021

Launch the Response Screening Platform

Launch the Response Screening platform by selecting Analyze > Screening > Response Screening.

Figure 22.3 Response Screening Launch Window 

Response Screening Launch Window

For more information about the options in the Select Columns red triangle menu, see Column Filter Menu in Using JMP.

Launch Window Roles

Y, Response

Identifies the response columns containing the measurements to be analyzed.


Identifies the columns against which you want to test the responses.


For each level of the specified column, analyzes the corresponding rows separately, but presents the results in a single table and report.


Identifies a column whose values assign a weight to each row. These values are used as weights in the analysis. See Weight in Fitting Linear Models.


Identifies a column whose values assign a frequency to each row. These values enable you to account for pre-summarized data. See Frequency in Fitting Linear Models.


For each level of the specified column, analyzes the corresponding Ys and Xs and presents the results in separate tables and reports.

Launch Window Options

PValues Table on Launch

Creates a data table for the p-values and individual model fit statistics.


For continuous responses, uses Huber M-estimates (Huber and Ronchetti 2009) in fitting regression and ANOVA models. This robust method down weights outliers to reduce the sensitivity of tests to outliers. If there are no outliers, these estimates are close to the least squares estimates. Note that this option increases processing time. See Fit Robust in Basic Analysis for more information about Huber M-estimation. For an example, see Example of Robust Fit.


Assumes that the errors have a Cauchy distribution. A Cauchy distribution has fatter tails than the normal distribution, resulting in a reduced emphasis on outliers. This option can be useful if you have a large proportion of outliers in your data. However, if your data are close to normal with only a few outliers, this option can lead to incorrect inferences. The Cauchy option estimates parameters using maximum likelihood and a Cauchy link function.

Poisson Y

Fits each Y response as a count having a Poisson distribution. The test is performed only for categorical X. This option is appropriate when your responses are counts.

Missing is category

For any categorical X variable, treats missing values on X as a category.

Paired X and Y

Performs tests only for Y columns paired with X columns according to their order in the Y, Response and X lists. The first Y is paired with the first X, the second Y with the second X, and so on.

Practical Difference Portion

The fraction of the specification range, or of an estimated six standard deviation range, that represents a difference that you consider pragmatically meaningful. If Spec Limits is not set as a column property, a range of six standard deviations is estimated for the response. The standard deviation estimate is computed from the interquartile range (IQR), as Equation shown here.

If no Practical Difference Proportion is specified, its value defaults to 0.10. Tests of practical significance and equivalence tests use this difference to determine the practical difference. See Compare Means Data Table.


Use to control the scale of plots involving LogWorth values (-log10 of p-values). LogWorth values that exceed MaxLogWorth are plotted as MaxLogWorth to prevent extreme scales in LogWorth plots. See Example of the MaxLogWorth Option for an example.

Advanced Options

Contains the following options:


Adds a new column called Kappa to the data table. If Y and X are both categorical and have the same levels, kappa is provided. This is a measure of agreement between Y and X.


The Corr option computes the Pearson product-moment correlation in terms of the indices defined by the value ordering.

If X and Y are both binary, the Pearson product-moment calculation gives Spearman’s rho and Kendall's Tau-b. Otherwise, a value of Corr that is large in magnitude indicates an association; a Corr value that is small in magnitude does not preclude an association.

Force X Categorical

Ignores the modeling type and treats all X columns as categorical.

Force X Continuous

Ignores the modeling type and treats all X columns as continuous.

Force Y Categorical

Ignores the modeling type and treats all Y columns as categorical.

Force Y Continuous

Ignores the modeling type and treats all Y columns as continuous.


Suppresses multithreading used for computational speed.

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