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methods.rho2hat

Methods for Intensity Functions of Two Spatial Covariates


Description

These are methods for the class "rho2hat".

Usage

## S3 method for class 'rho2hat'
plot(x, ..., do.points=FALSE)

## S3 method for class 'rho2hat'
print(x, ...)

## S3 method for class 'rho2hat'
predict(object, ..., relative=FALSE)

Arguments

x,object

An object of class "rho2hat".

...

Arguments passed to other methods.

do.points

Logical value indicating whether to plot the observed values of the covariates at the data points.

relative

Logical value indicating whether to compute the estimated point process intensity (relative=FALSE) or the relative risk (relative=TRUE) in the case of a relative risk estimate.

Details

These functions are methods for the generic commands print, predict and plot for the class "rho2hat".

An object of class "rho2hat" is an estimate of the intensity of a point process, as a function of two given spatial covariates. See rho2hat.

The method plot.rho2hat displays the estimated function rho using plot.fv, and optionally adds a rug plot of the observed values of the covariate. In this plot the two axes represent possible values of the two covariates.

The method predict.rho2hat computes a pixel image of the intensity rho(Z1(u), Z2(u)) at each spatial location u, where Z1(u) and Z2(u) are the two spatial covariates.

Value

For predict.rho2hat the value is a pixel image (object of class "im"). For other functions, the value is NULL.

Author(s)

See Also

Examples

r2 <- with(bei.extra, rho2hat(bei, elev, grad))
  r2
  plot(r2)
  plot(predict(r2))

spatstat.core

Core Functionality of the 'spatstat' Family

v2.1-2
GPL (>= 2)
Authors
Adrian Baddeley [aut, cre], Rolf Turner [aut], Ege Rubak [aut], Kasper Klitgaard Berthelsen [ctb], Achmad Choiruddin [ctb], Jean-Francois Coeurjolly [ctb], Ottmar Cronie [ctb], Tilman Davies [ctb], Julian Gilbey [ctb], Yongtao Guan [ctb], Ute Hahn [ctb], Kassel Hingee [ctb], Abdollah Jalilian [ctb], Marie-Colette van Lieshout [ctb], Greg McSwiggan [ctb], Tuomas Rajala [ctb], Suman Rakshit [ctb], Dominic Schuhmacher [ctb], Rasmus Plenge Waagepetersen [ctb], Hangsheng Wang [ctb]
Initial release
2021-04-17

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