# mApply

##### Apply a Function to Rows of a Matrix or Vector

`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.

##### Usage

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

##### Arguments

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

`FUN`

argument- 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

`TRUE`

to keep result as a matrix even if`simplify`

is`TRUE`

, in the case of only one stratum

##### Value

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`

.

##### See Also

`asNumericMatrix`

, `matrix2dataFrame`

, `tapply`

,
`sapply`

, `lapply`

, `mapply`

, `by`

.

##### Examples

```
# NOT RUN {
require(datasets, TRUE)
a <- mApply(iris[,-5], iris$Species, mean)
# }
```

*Documentation reproduced from package Hmisc, version 4.3-1, License: GPL (>= 2)*