Returns an array of cells of a time series after removing all missing values.

## Syntax

**RMNA**(**X**, Y)

**X**- is the input data sample (one- or two-dimensional array of cells).
**Y**- is an auxiliary (optional) data sample (one- or two-dimensional array of cells).

## Remarks

- Each input (or auxiliary) data set column corresponds to a separate variable.
- Each row in the input (or in the auxiliary) data set corresponds to an observation.
- The auxiliary data set (Y) is optional and may include one or more variables (columns).
- The auxiliary data set input is required when both X and Y define observations. For example, consider a multiple regression data set where X corresponds to the explanatory variables, and Y is the response variable.
- When an auxiliary data set is present, the number of rows of the input data set (X) must equal the number of rows of the auxiliary data set (Y).
- Observations (i.e., rows) with missing values (e.g., #N/A, #VALUE!, #NUM!, empty cell) in either X or Y are removed.
- The function RMNA preserves the data set's original orders (rows and columns) (X).
- Using the RMNA function with a time-series (univariate or multivariate) data set is problematic, as the output data set may be unevenly spaced (over time).
- For the time series-based data set, it is recommended to use a time index (e.g., timestamp) as one of the variables (columns) in the input data set.
- The function RMNA has been revised in NumXL MARTHA version 1.67.

## Files Examples

## Related Links

- NumXL Tips & Tricks Blog - Removing Missing Values (RMNA).
- What to do About Missing Values in Time Series Cross-Section Data, James Honaker and Gary King, American Journal of Political Science.

## References

- Hamilton, J.D.; Time Series Analysis, Princeton University Press (1994), ISBN 0-691-04289-6.
- Kenney, J. F. and Keeping, E. S. (1962) "Linear Regression and Correlation." Ch. 15 in Mathematics of Statistics, Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252-285.

## Comments

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