Fit Point Process Model to Point Pattern on Linear Network
Fit a point process model to a point pattern dataset on a linear network
lppm(X, ...) ## S3 method for class 'formula' lppm(X, interaction=NULL, ..., data=NULL) ## S3 method for class 'lpp' lppm(X, ..., eps=NULL, nd=1000, random=FALSE)
X |
Either an object of class |
... |
Arguments passed to |
interaction |
An object of class |
data |
Optional. The values of spatial covariates (other than the Cartesian coordinates) required by the model. A list whose entries are images, functions, windows, tessellations or single numbers. |
eps |
Optional. Spacing between dummy points along each segment of the network. |
nd |
Optional. Total number of dummy points placed on
the network. Ignored if |
random |
Logical value indicating whether the grid of dummy points should be placed at a randomised starting position. |
This function fits a point process model to data that specify
a point pattern on a linear network. It is a counterpart of
the model-fitting function ppm designed
to work with objects of class "lpp" instead of "ppp".
The function lppm is generic, with methods for
the classes formula and lppp.
In lppm.lpp
the first argument X should be an object of class "lpp"
(created by the command lpp) specifying a point pattern
on a linear network.
In lppm.formula,
the first argument is a formula in the R language
describing the spatial trend model to be fitted. It has the general form
pattern ~ trend where the left hand side pattern is usually
the name of a point pattern on a linear network
(object of class "lpp")
to which the model should be fitted, or an expression which evaluates
to such a point pattern;
and the right hand side trend is an expression specifying the
spatial trend of the model.
Other arguments ... are passed from lppm.formula
to lppm.lpp and from lppm.lpp to ppm.
An object of class "lppm" representing the fitted model.
There are methods for print, predict,
coef and similar functions.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Greg McSwiggan.
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.
X <- runiflpp(15, simplenet) lppm(X ~1) lppm(X ~x) marks(X) <- factor(rep(letters[1:3], 5)) lppm(X ~ marks) lppm(X ~ marks * x)
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