Launch the Response Screening Platform

JMP Support 800.450.0135 (US)

Documentation Feedback
Your feedback is important to us. us any comments about our documentation.

Predictive and Specialized Modeling • Response Screening • Launch the Response Screening Platform

•

Launch the Response Screening Platform

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

Figure 17.4 Response Screening Launch Window

Launch Window Roles

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. For details, see Weight in the Fitting Linear Models book.

Identifies a column whose values assign a frequency to each row. These values enable you to account for pre-summarized data. For details, see Frequency in the Fitting Linear Models book.

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

For continuous responses, uses robust (Huber) estimation to down weight outliers. If there are no outliers, these estimates are close to the least squares estimates. Note that this option increases processing time.

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.

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

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.

Aligns all the Y responses to the same scale when you run individual analyses using the report’s Fit Selected Items options.

Missing is category

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

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.

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.

Suppresses multithreading.

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

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.

Conducts the analysis and displays the results.

Closes the launch window.

Removes the selected variable from the assigned role.

Populates the launch window with the previous model specification that you ran.

Opens the Help topics for the Response Screening launch window.

•

Help created on 7/12/2018