Statistics: rmmissing

### Function Reference: `rmmissing`

statistics: R = rmmissing (A)
statistics: R = rmmissing (A, dim)
statistics: R = rmmissing (…, Name, Value)
statistics: [R TF] = rmmissing (…)

Remove missing or incomplete data from an array.

Given an input vector or matrix (2-D array) A, remove missing data from a vector or missing rows or columns from a matrix. A can be a numeric array, char array, or an array of cell strings. R returns the array after removal of missing data.

The values which represent missing data depend on the data type of A:

- `NaN`: `single`, `double`.
- `' '` (white space): `char`.
- `{''}`: string cells.

Choose to remove rows (default) or columns by setting optional input dim:

- `1`: rows.
- `2`: columns.

Note: data types with no default ’missing’ value will always result in `R == A` and a TF output of `false(size(A))`.

Additional optional parameters are set by Name-Value pairs. These are:

- `MinNumMissing`: minimum number of missing values to remove an entry, row or column, defined as a positive integer number. E.g.: if `MinNumMissing` is set to `2`, remove the row of a numeric matrix only if it includes 2 or more NaN.

Optional return value TF is a logical array where `true` values represent removed entries, rows or columns from the original data A.

See also: fillmissing, ismissing, standardizeMissing

Source Code: rmmissing