| mApply {Hmisc} | 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 if attributes and storage modes of the original variables are
saved by calling subsAttr. subsAttr saves attributes that
are commonly preserved across row subsetting (i.e., it does not save
dim, dimnames, or names attributes).
mApply(X, INDEX, FUN, ..., simplify=TRUE)
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. |
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
f.harrell@vanderbilt.edu
asNumericMatrix, matrix2dataFrame, tapply,
sapply, lapply, mapply, by.
require(datasets, TRUE) a <- mApply(iris[,-5], iris$Species, mean)