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Text version of the page
| Missing Data | |||||||||||||||||||||||||||||||||||||||||||
| Notice that the sum of the elements in the middle column is a NaN value because that column contains a NaN. If you do not want to have NaNs in your final results, you must remove these values from your data. For more information, see "Removing NaNs from Data" on page 1-7. | |||||||||||||||||||||||||||||||||||||||||||
| Removing NaNs from Data You can use the IEEE function isnan to identify NaNs in the data, and then remove them using the techniques in the following table. | |||||||||||||||||||||||||||||||||||||||||||
| Note You must use the function isnan to identify NaNsbecause,byIEEE arithmetic convention, the logical comparison NaN == NaN always produces 0 (i.e., it never evaluates to true). Therefore, you cannot use x(x==NaN) = [] to remove NaNs from your data. | |||||||||||||||||||||||||||||||||||||||||||
| |||||||||||||||||||||||||||||||||||||||||||
| If you frequently need to remove NaNs, you might want to write a short M-file function that you can call: function X = exciseRows(X) | |||||||||||||||||||||||||||||||||||||||||||
| 1-7 | |||||||||||||||||||||||||||||||||||||||||||