This article describes how any outputs saved within Displayr from advanced analyses can be audited to review how they were created and identify any issues.

## Requirements

A Displayr analysis using advanced methods. The following methods are referenced in the following section:

- MaxDiff
- Choice Modeling
- Latent Class Analysis
- Trees
- Cluster Analysis
- Principal Component Analysis
- Multiple Correspondence Analysis

## Method

### Reviewing the dependency graph

By far, the easiest way to audit your analyses and variables is by reviewing their dependency graphs. You can right click on an output on the page or a variable and select **Dependency graph** to see the upstream and downstream dependencies in a more visual map. See Viewing Dependency Graphs to Understand Calculations for more info.

### Segments created using trees

The segments variable set created by the tree will be labeled the same as the **Tree** output on the page. You can search the document for the label to find the **Tree** that created the **Segments** variable. The settings used to create a Latent Class Analysis or Other Trees can be accessed by clicking on the **Tree** output and selecting **Modify** in the **object inspector** on the right.

### Constructed variables

Many of the advanced analysis methods that are R-based have a section in their **object inspector** for **SAVE VARIABLE **(e.g., predicted scores from *Regression*, segments from *Cluster Analysis* and *Latent Class Analysis*, factor scores from *Principal Components Analysis* and *Multiple Correspondence Analysis). *You can review the R CODE in the **object inspector** of the variable to see which model is referenced in the code.

### Experiment variable sets

Experiment variable sets can do exotic *MaxDiff* (such as anchored) and legacy *Choice Modeling.* They are audited like any other variable set, see How to Review Data in Tables and Variables.

## Next

How to Review Data in Tables and Variables