---
title: "Troubleshooting Common Issues"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Troubleshooting Common Issues}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Common Error Messages and Solutions

### "Auxiliary regression failed"
**Cause:** Perfect multicollinearity in auxiliary regression
**Solution:** Check for duplicated or linearly dependent predictors in the
auxiliary regression. Removing or combining offending variables usually
resolves the issue.

### "Log of negative values"
**Cause:** Negative values in variable used for log transformation
**Solution:** Ensure the variable is strictly positive before applying a
log transformation or add a small constant to shift the data.

## Performance Issues

### Large Datasets
For very large data sets consider using `performWhiteTestStreaming()` or
running diagnostics on a representative sample to reduce computation
time.

### Convergence Problems
Numerical issues may arise with extreme multicollinearity or poorly
scaled variables. Rescaling predictors or using robust optimisation
methods can help.

## Interpretation Guidelines

### When Tests Disagree
No single test is uniformly most powerful. Examine residual plots and
consider the nature of your data when diagnostics give conflicting
results.

### Power Considerations
Some tests have low power in small samples. Simulation via
`simulate_power_analysis()` can help determine the best approach for a
given situation.