Creates an object of class "linim" that represents
a pixel image on a linear network.
linim(L, Z, …, restrict=TRUE, df=NULL)Linear network (object of class "linnet").
Pixel image (object of class "im").
Ignored.
Advanced use only.
Logical value indicating whether to ensure that all pixels in Z
which do not lie on the network L have pixel value NA.
This condition must be satisfied, but if you set
restrict=FALSE it will not be checked, and the code will
run faster.
Advanced use only. Data frame giving full details of the mapping between
the pixels of Z and the lines of L.
See Details.
Object of class "linim" that also inherits the class
"im".
There is a special method for plotting this class.
This command creates an object of class "linim" that represents
a pixel image defined on a linear network.
Typically such objects are
used to represent the result of smoothing or model-fitting on the
network. Most users will not need to call linim directly.
The argument L is a linear network (object of class "linnet").
It gives the exact spatial locations
of the line segments of the network, and their connectivity.
The argument Z is a pixel image object of class "im"
that gives a pixellated approximation of the function values.
For increased efficiency, advanced users may specify the
optional argument df. This is a data frame giving the
precomputed mapping between the pixels of Z
and the line segments of L.
It should have columns named xc, yc containing the coordinates of
the pixel centres, x,y containing the projections of these
pixel centres onto the linear network, mapXY identifying the
line segment on which each projected point lies, and tp giving
the parametric position of (x,y) along the segment.
Ang, Q.W. (2010) Statistical methodology for events on a network. Master's thesis, School of Mathematics and Statistics, University of Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012) Geometrically corrected second-order analysis of events on a linear network, with applications to ecology and criminology. Scandinavian Journal of Statistics 39, 591--617.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.
# NOT RUN {
Z <- as.im(function(x,y) {x-y}, Frame(simplenet))
X <- linim(simplenet, Z)
X
# }
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