When you have incomplete data, simply filling gaps with an average or median can distort your results. Multiple imputation MICE fills missing values by looking at how other variables relate to each other, creating a more complete picture of your dataset without needing to discard rows that contain missing information.
Handling Missing Data Properly
In many datasets, information is missing for various reasons. Often, the easiest approach is to delete those rows or fill them with a single value like the column average. However, these methods can bias your results or throw away valuable data from other columns.
Multiple imputation MICE offers a smarter path. Instead of guessing a single value for every missing cell, it uses an iterative process to estimate missing values based on the distribution of your other available data. This keeps your sample size intact and reflects the reality of your variables.
How the Process Works
Lattice runs this method by treating each column with missing data as a variable to be predicted. It cycles through these columns, using the others as predictors. By repeating this process, the filled values align with the statistical relationships found in your actual data.
When the tool finishes, it provides a summary comparing the number of missing entries before and after the process. It also reports the mean, minimum, and maximum values of the filled entries, giving you a sanity check to ensure the imputed values make logical sense.
Preserving Data Integrity
Lattice follows a strict 'no-contamination' policy. When you run multiple imputation MICE, the system generates a new dataset ID for your results. You can then use this new ID for any subsequent analysis, plotting, or modeling, ensuring your original raw data is always available for reference.
The tool is designed to catch errors before they propagate. If a column is non-numeric, contains no data at all, or has other structural issues, the system will alert you, preventing accidental miscalculations.
1 · Intent → method
An LLM picks data_multiple_imputation from a fixed catalog.
2 · Method → numbers
Deterministic Python engine runs the math. Same input → same output.
3 · Numbers → plain language
A second LLM translates the result into your domain’s vocabulary.
Is multiple imputation MICE better than just filling with the mean?
Filling with the mean assumes every missing value is exactly average, which isn't realistic. Multiple imputation MICE looks at your other data to predict what those missing values should be, helping to preserve the original patterns and correlations in your analysis.
Will this tool change my original file?
No. Following Lattice's design principles, this tool creates a new derived dataset. Your original data remains untouched, and you can compare the original and the new version at any time.
Tool input schema
Schema for data_multiple_imputation not exported yet (run pnpm export:registry).