## Introduction

This article describes how to edit the underlying **Standard R** code in an R-based output. You will know an output is based in R by the R icon next to the output in the Report tree. Standard R code is written by our developers and encompasses many R outputs. We enable users to further customize our Standard R if they need to directly on the output through the **Properties > R CODE** box. Keep in mind with doing this, we can't guarantee the output will continue to work as expected since adding custom code may interfere with the Standard R. Proceed with caution!

## Requirements

An R-based analysis tool or visualization (denoted by the R icon in the Report tree) that was created via the menus. In this example, we will modify a choice model output for Hierarchical Bayes (created via **Anything > Advanced Analysis > Choice Modeling > Hierarchical Bayes**) by specifying prior values for the mean parameters. This allows us to impose a constraint on the mean coefficients that the analysis outputs.

## Method

1. Select your analysis tool in the **Report** tree. In this case, our Hierarchical Bayes output.

2. Go to **Properties > R CODE** in the **object inspector**.

3. Scroll down to the bottom where the main function is. Here, the main function is called `FitChoiceModel`

.

4. If you hover your cursor over the function name, a preview of the R documentation will appear with all the available arguments.

5. Add the following argument line within this function (i.e. before the last line):

hb.prior.mean = c(1, 2, 1, 0, 1, 0, 1, 2, 0),

Assuming you have 9 mean coefficients in your output, this will apply the specified priors to your analysis. The default is 0. Note, that `hb.prior.sd`

is the equivalent argument for specifying priors for standard deviations.

6. Press **Yes** when prompted to edit the Standard R code.

7. Press **Calculate** to run or update your analysis.

## Notes

While the example above is very specific, you can edit any part of the R code used to create the output. In addition to editing settings passed through to the analysis/visualization function, you can also create custom code to modify the input data before the analysis or the final output shown and create more custom error handling.