plot.loccit {spatstat.local}

Plot a Locally Fitted Cluster or Cox Point Process Model

Description

Plot an object of class "loccit" representing a locally-fitted cluster or Cox point process model.

Usage

## S3 method for class 'loccit'
plot(x, ...,
               what = c("modelpar", "coefs", "lambda"),
               how = c("smoothed", "exact"), which = NULL,
               pre=NULL, post=NULL)

Arguments

x

The model to be plotted. A locally-fitted cluster or Cox point process model (object of class "loccit").

...

Arguments passed to plot.ppp or plot.im to control the plot.

what

Character string determining which quantities to display: "modelpar" for the cluster model parameters, "coefs" for the trend coefficients, or "lambda" for the fitted intensity.

how

Character string determining whether to display the fitted parameter values at the data points (how="exact") or the smoothed fitted parameters as pixel images (how="smoothed").

which

Optional. Which component(s) of the vector-valued quantity to display. An index or index vector. Default is to plot all components.

pre, post

Transformations to apply before and after smoothing.

Details

This is a method for the generic command plot for the class "loccit".

The argument which, if present, specifies which fitted parameters are displayed. It may be any kind of index for a numeric vector.

The quantities are computed at irregularly-placed points. If how="exact" the exact computed values will be displayed as circles centred at the locations where they were computed. If how="smoothed" these values will be kernel-smoothed using Smooth.ppp and displayed as a pixel image.

Value

NULL.

Author(s)

Adrian Baddeley [e-mail].

References

Baddeley, A. (2017) Local composite likelihood for spatial point patterns. Spatial Statistics 22, 261–295. DOI: 10.1016/j.spasta.2017.03.001

Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. Chapman and Hall/CRC Press.

See Also

loccit, plot, plot.default

Examples

   X <- redwood[owin(c(0,1), c(-1,-1/2))]
   fitc <- loccit(X, ~1, "Thomas", nd=5, control=list(maxit=20))
   plot(fitc, how="exact")  
   plot(fitc, how="smoothed")