Publication date: 10/01/2019

Response Screening

Test Many Responses in Large-Scale Data

The analysis of large-scale data sets, where hundreds or thousands of measurements are taken on a part or an organism, requires innovative approaches. But testing many responses for the effects of factors can be challenging, if not misleading, without appropriate methodology.

Response Screening automates the process of conducting tests across a large number of responses. Your test results and summary statistics are presented in data tables, rather than reports, to enable data exploration. A False-Discovery Rate approach guards against incorrect declarations of significance. Plots of p-values are scaled using the LogWorth, making them easily interpretable.

Because large scale data sets are often messy, Response Screening presents methods that address irregularly distributed and missing data. A robust estimate method allows outliers to remain in the data, but reduces the sensitivity of tests to these outliers. Missing data options allow missing values to be included in the analysis. These features enable you to analyze your data without first conducting an extensive analysis of data quality.

When you have many observations, even differences that are of no practical interest can be statistically significant. Response Screening presents tests of practical difference, where you specify the difference that you are interested in detecting. On the other hand, you might want to know whether differences do not exceed a given magnitude, that is, if the means are equivalent. For this purpose, Response Screening presents equivalence tests.

Figure 21.1 Example of a Response Screening PlotĀ 

Contents

Overview of the Response Screening Platform

Example of Response Screening

Launch the Response Screening Platform

The Response Screening Report

FDR PValue Plot
FDR LogWorth by Effect Size
FDR LogWorth by RSquare

The PValues Data Table

PValues Data Table Columns
Columns Added for Robust Option
PValues Data Table Scripts

Response Screening Platform Options

Means Data Table
Compare Means Data Table

The Response Screening Personality in Fit Model

Launch Response Screening in Fit Model
The Fit Response Screening Report
PValues Data Table
Y Fits Data Table

Additional Examples of Response Screening

Example of Tests of Practical Significance and Equivalence
Example of the MaxLogWorth Option
Example of Robust Fit
Response Screening Personality

Statistical Details for the Response Screening Platform

The False Discovery Rate
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