mApply | R Documentation |

`mApply`

is like `tapply`

except that the first argument can
be a matrix or a vector, and the output is cleaned up if `simplify=TRUE`

.
It uses code adapted from Tony Plate (tplate@blackmesacapital.com) to
operate on grouped submatrices.

As `mApply`

can be much faster than using `by`

, it is often
worth the trouble of converting a data frame to a numeric matrix for
processing by `mApply`

. `asNumericMatrix`

will do this, and
`matrix2dataFrame`

will convert a numeric matrix back into a data
frame.

```
mApply(X, INDEX, FUN, ..., simplify=TRUE, keepmatrix=FALSE)
```

`X` |
a vector or matrix capable of being operated on by the
function specified as the |

`INDEX` |
list of factors, each of same number of rows as 'X' has. |

`FUN` |
the function to be applied. In the case of functions like '+', ' |

`...` |
optional arguments to 'FUN'. |

`simplify` |
set to 'FALSE' to suppress simplification of the result in to an array, matrix, etc. |

`keepmatrix` |
set to |

For `mApply`

, the returned value is a vector, matrix, or list.
If `FUN`

returns more than one number, the result is an array if
`simplify=TRUE`

and is a list otherwise. If a matrix is returned,
its rows correspond to unique combinations of `INDEX`

. If
`INDEX`

is a list with more than one vector, `FUN`

returns
more than one number, and `simplify=FALSE`

, the returned value is a
list that is an array with the first dimension corresponding to the last
vector in `INDEX`

, the second dimension corresponding to the next
to last vector in `INDEX`

, etc., and the elements of the list-array
correspond to the values computed by `FUN`

. In this situation the
returned value is a regular array if `simplify=TRUE`

. The order
of dimensions is as previously but the additional (last) dimension
corresponds to values computed by `FUN`

.

Frank Harrell

Department of Biostatistics

Vanderbilt University

fh@fharrell.com

`asNumericMatrix`

, `matrix2dataFrame`

, `tapply`

,
`sapply`

, `lapply`

, `mapply`

, `by`

.

```
require(datasets, TRUE)
a <- mApply(iris[,-5], iris$Species, mean)
```

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