amap {garray}

Mapping matching dimension of arrays with a function

Description

Generalized and smart mapply()/Map()/outer()/sweep() for data mapping. Matching is checked automatically.

Usage

amap(FUN, ..., MoreArgs = NULL, SIMPLIFY = TRUE, VECTORIZED = NA)

## S3 method for class 'garray'
Ops(e1, e2)

Arguments

FUN

Known vectorized function is recognized if passed in as character. Other functions will be vectorized by .mapply().

...

Arrays with margins (names of dimnames) and maybe with sdim. Orders of their margins can be different, but the extent along a margin is matched. Unmatched margins are broadcasting like outer(). Scalar (length 1 vector) do not contribute margins and not broadcast here (they will broadcast by .mapply() later).

MoreArgs

a list of other arguments to 'FUN', no matching of margins.

SIMPLIFY

logical, attempt to reduce the result to exclude recursive structure (no list hierachy but plain generalized array).

VECTORIZED

Whether FUN is vectorized will affect the behaviours. Some combination of FUN and VECTORIZED is not simply slowing down, but produces meaningless results or even stop (e.g., cumsum). TRUE - call FUN once with arrays being reorganize on dimensions; FALSE - call FUN many times (via .mapply), with each cell of arrays.

e1, e2

Generalized arrays, being operands.

Value

The dimensions is deduced from inputs.

Examples

a <- garray(1:24, c(4,6,2), dimnames=list(X=1:4, Y=letters[1:6], Z=NULL),
	sdim=list(XX=c(x1=3,x2=1), YY=c(y1=1,y2=2)))
b <- garray(1:6/10,6,dimnames=list(Y=letters[1:6]))
c <- garray(1:4/100,c(X=4))
d <- garray(1:4/1000,c(Y=4))
e <- garray(1:2/1000,c(X=2))
f <- garray(0,c(Z=2))
g <- garray(0,c(ZZ=2))
m1 <- amap(psummary,c,a,b,      0.0001, VECTORIZED=FALSE)
m2 <- amap(sum,     c,a,b,      0.0001, VECTORIZED=FALSE)
m3 <-               c+a+b+      0.0001
n1 <- amap(sum,     c,a,b,d,    0.0001, VECTORIZED=FALSE)
n2 <- amap(sum,     c,a,b,e,    0.0001, VECTORIZED=FALSE)
n3 <- amap(sum,     c,a,b,e,f,  0.0001, VECTORIZED=FALSE)
p1 <- amap(sum,     c,a,b,e,f,g,0.0001, VECTORIZED=FALSE)
q1 <- amap(sum, c,a,b,e,f,g,0.0001, SIMPLIFY=FALSE, VECTORIZED=FALSE)
q2 <- amap(c,   c,a,b,e,f,g,0.0001, SIMPLIFY=FALSE, VECTORIZED=FALSE)
q3 <- amap(list,c,a,b,e,f,g,0.0001, SIMPLIFY=FALSE, VECTORIZED=FALSE)
m1==m2
m2==m3
m2==aperm(m3, 3:1)