binMean2D {oce} | R Documentation |
Bin-average f=f(x,y)
Description
Average the values of a vector f(x,y)
in bins defined on
vectors x
and y
. A common example might be averaging
spatial data into location bins.
Usage
binMean2D(
x,
y,
f,
xbreaks,
ybreaks,
flatten = FALSE,
fill = FALSE,
fillgap = -1,
include.lowest = FALSE,
na.rm = FALSE
)
Arguments
x |
vector of numerical values. |
y |
vector of numerical values. |
f |
Matrix of numerical values, a matrix f=f(x,y). |
xbreaks |
Vector of values of |
ybreaks |
Vector of values of |
flatten |
A logical value indicating whether
the return value also contains equilength
vectors |
fill |
Logical value indicating whether to fill |
fillgap |
Integer controlling the size of gap that can be filled across. If this is negative (as in the default), gaps will be filled regardless of their size. If it is positive, then gaps exceeding this number of indices will not be filled. |
include.lowest |
logical value indicating whether to include
|
na.rm |
logical value indicating whether to remove NA values before
doing the computation of the average. This is passed to |
Value
By default, i.e. with flatten
being FALSE, binMean2D()
returns a
list with the following elements: xmids
, a vector holding the x-bin midpoints;
ymids
, a vector holding the y-bin midpoints; number
, a matrix holding the
number the points in each bin; and result
, a matrix holding the mean
value in each bin. If flatten
is TRUE, the number
and result
matrices are renamed as n
and f
and transformed to vectors, while
the bin midpoints are renamed as x
and y
and extended to match the length
of n
and f
.
Author(s)
Dan Kelley
See Also
Other bin-related functions:
binApply1D()
,
binApply2D()
,
binAverage()
,
binCount1D()
,
binCount2D()
,
binMean1D()
Examples
library(oce)
x <- runif(500, 0, 0.5)
y <- runif(500, 0, 0.5)
f <- x^2 + y^2
xb <- seq(0, 0.5, 0.1)
yb <- seq(0, 0.5, 0.1)
m <- binMean2D(x, y, f, xb, yb)
cm <- colormap(f, col = oceColorsTurbo)
opar <- par(no.readonly = TRUE)
drawPalette(colormap = cm)
plot(x, y, col = cm$zcol, pch = 20, cex = 1.4)
contour(m$xmids, m$ymids, m$result, add = TRUE, labcex = 1.4)
par(opar)