colBinnedSmoothing.matrix {aroma.core} | R Documentation |
Binned smoothing of a matrix column by column
Description
Binned smoothing of a matrix column by column.
Usage
## S3 method for class 'matrix'
colBinnedSmoothing(Y, x=seq_len(nrow(Y)), w=NULL, xOut=NULL, xOutRange=NULL,
from=min(x, na.rm = TRUE), to=max(x, na.rm = TRUE), by=NULL, length.out=length(x),
na.rm=TRUE, FUN="median", ..., verbose=FALSE)
Arguments
Y |
|
x |
A (optional) |
w |
A optional |
xOut |
|
xOutRange |
Optional Kx2 |
from , to , by , length.out |
If neither |
FUN |
A |
na.rm |
If |
... |
Not used. |
verbose |
See |
Details
Note that all zero-length bins [x0,x1)
will get result
in an NA
value, because such bins contain no data points.
This also means that colBinnedSmoothing(Y, x=x, xOut=xOut)
where xOut
contains duplicated values, will result in
some zero-length bins and hence NA
values.
Value
Returns a numeric
KxI matrix
(or a vector
of length K) where
K is the total number of bins.
The following attributes are also returned:
xOut
The center locations of each bin.
xOutRange
The bin boundaries.
count
The number of data points within each bin (based solely on argument
x
).binWidth
The average bin width.
Author(s)
Henrik Bengtsson
See Also
Examples
# Number of tracks
I <- 4
# Number of data points per track
J <- 100
# Simulate data with a gain in track 2 and 3
x <- 1:J
Y <- matrix(rnorm(I*J, sd=1/2), ncol=I)
Y[30:50,2:3] <- Y[30:50,2:3] + 3
# Uniformly distributed equal-sized bins
Ys3 <- colBinnedSmoothing(Y, x=x, from=2, by=3)
Ys5 <- colBinnedSmoothing(Y, x=x, from=3, by=5)
# Custom bins
xOutRange <- t(matrix(c(
1, 11,
11, 31,
31, 41,
41, 51,
51, 81,
81, 91,
91,101
), nrow=2))
YsC <- colBinnedSmoothing(Y, x=x, xOutRange=xOutRange)
# Custom bins specified by center locations with
# maximized width relative to the neighboring bins.
xOut <- c(6, 21, 36, 46, 66, 86, 96)
YsD <- colBinnedSmoothing(Y, x=x, xOut=xOut)
xlim <- range(x)
ylim <- c(-3,5)
layout(matrix(1:I, ncol=1))
par(mar=c(3,3,1,1)+0.1, pch=19)
for (ii in 1:I) {
plot(NA, xlim=xlim, ylim=ylim)
points(x, Y[,ii], col="#999999")
xOut <- attr(Ys3, "xOut")
lines(xOut, Ys3[,ii], col=2)
points(xOut, Ys3[,ii], col=2)
xOut <- attr(Ys5, "xOut")
lines(xOut, Ys5[,ii], col=3)
points(xOut, Ys5[,ii], col=3)
xOut <- attr(YsC, "xOut")
lines(xOut, YsC[,ii], col=4)
points(xOut, YsC[,ii], col=4, pch=15)
xOut <- attr(YsD, "xOut")
lines(xOut, YsD[,ii], col=5)
points(xOut, YsD[,ii], col=5, pch=15)
if (ii == 1) {
legend("topright", pch=c(19,19,15,15), col=c(2,3,4,5),
c("by=3", "by=5", "Custom #1", "Custom #2"), horiz=TRUE, bty="n")
}
}
# Sanity checks
xOut <- x
YsT <- colBinnedSmoothing(Y, x=x, xOut=xOut)
stopifnot(all(YsT == Y))
stopifnot(all(attr(YsT, "counts") == 1))
xOut <- attr(YsD, "xOut")
YsE <- colBinnedSmoothing(YsD, x=xOut, xOut=xOut)
stopifnot(all(YsE == YsD))
stopifnot(all(attr(YsE, "xOutRange") == attr(YsD, "xOutRange")))
stopifnot(all(attr(YsE, "counts") == 1))
# Scramble ordering of loci
idxs <- sample(x)
x2 <- x[idxs]
Y2 <- Y[idxs,,drop=FALSE]
Y2s <- colBinnedSmoothing(Y2, x=x2, xOut=x2)
stopifnot(all(attr(Y2s, "xOut") == x2))
stopifnot(all(attr(Y2s, "counts") == 1))
stopifnot(all(Y2s == Y2))
xOut <- x[seq(from=2, to=J, by=3)]
YsT <- colBinnedSmoothing(Y, x=x, xOut=xOut)
stopifnot(all(YsT == Ys3))
stopifnot(all(attr(YsT, "counts") == 3))
xOut <- x[seq(from=3, to=J, by=5)]
YsT <- colBinnedSmoothing(Y, x=x, xOut=xOut)
stopifnot(all(YsT == Ys5))
stopifnot(all(attr(YsT, "counts") == 5))