Converts various kinds of data to a pixel image on a linear network.
as.linim(X, ...) # S3 method for linim
as.linim(X, ...)
# S3 method for default
as.linim(X, L, ...,
eps = NULL, dimyx = NULL, xy = NULL,
rule.eps=c("adjust.eps",
"grow.frame", "shrink.frame"),
delta=NULL, nd=NULL)
# S3 method for linfun
as.linim(X, L=domain(X), ...,
eps = NULL, dimyx = NULL, xy = NULL,
rule.eps=c("adjust.eps",
"grow.frame", "shrink.frame"),
delta=NULL, nd=NULL)
An image object on a linear network; an object of class "linim"
.
Data to be converted to a pixel image on a linear network.
Linear network (object of class "linnet"
).
Additional arguments passed to X
when X
is a function.
Optional arguments passed to as.mask
to control
the pixel resolution.
Optional. Numeric value giving the approximate distance (in coordinate units) between successive sample points along each segment of the network.
Optional. Integer giving the (approximate) number of
sample points on the network. Ignored if delta
is given.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net and Ege Rubak rubak@math.aau.dk
This function converts the data X
into a pixel image
on a linear network, an object of class "linim"
(see linim
).
The argument X
may be any of the following:
a function on a linear network, an object of class "linfun"
.
a pixel image on a linear network, an object of class
"linim"
.
a pixel image, an object of class "im"
.
any type of data acceptable to as.im
,
such as a function, numeric value, or window.
First X
is converted to a pixel image object Y
(object of class "im"
).
The conversion is performed by as.im
.
The arguments eps
, dimyx
, xy
and rule.eps
determine the pixel resolution.
Next Y
is converted to a pixel image on a linear network
using linim
. The argument L
determines the
linear network. If L
is missing or NULL
,
then X
should be an object of class "linim"
,
and L
defaults to the linear network on which X
is defined.
In addition to converting the
function to a pixel image, the algorithm also generates a fine grid of
sample points evenly spaced along each segment of the network
(with spacing at most delta
coordinate units). The function values
at these sample points are stored in the resulting object as a data frame
(the argument df
of linim
). This mechanism allows
greater accuracy for some calculations (such as
integral.linim
).
as.im
f <- function(x,y){ x + y }
plot(as.linim(f, simplenet))
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