methods.rhohat | R Documentation |
These are methods for the class "rhohat"
.
## S3 method for class 'rhohat' print(x, ...) ## S3 method for class 'rhohat' plot(x, ..., do.rug=TRUE) ## S3 method for class 'rhohat' predict(object, ..., relative=FALSE, what=c("rho", "lo", "hi", "se")) ## S3 method for class 'rhohat' simulate(object, nsim=1, ..., drop=TRUE)
x,object |
An object of class |
... |
Arguments passed to other methods. |
do.rug |
Logical value indicating whether to plot the observed values of the covariate as a rug plot along the horizontal axis. |
relative |
Logical value indicating whether to compute the
estimated point process intensity ( |
nsim |
Number of simulations to be generated. |
drop |
Logical value indicating what to do when |
what |
Optional character string (partially matched) specifying which
value should be calculated: either the function estimate ( |
These functions are methods for the generic commands
print
,
plot
,
predict
and
simulate
for the class "rhohat"
.
An object of class "rhohat"
is an estimate
of the intensity of a point process, as a function of a
given spatial covariate. See rhohat
.
The method plot.rhohat
displays the estimated function
rho using plot.fv
, and optionally
adds a rug
plot of the observed values of the covariate.
The method predict.rhohat
computes a pixel image of the
intensity rho(Z(u)) at each spatial location
u, where Z is the spatial covariate.
The method simulate.rhohat
invokes predict.rhohat
to determine the predicted intensity, and then simulates a
Poisson point process with this intensity.
For predict.rhohat
the value is a pixel image
(object of class "im"
or "linim"
).
For simulate.rhohat
the value is a point pattern
(object of class "ppp"
or "lpp"
).
For other functions, the value is NULL
.
rhohat
X <- rpoispp(function(x,y){exp(3+3*x)}) rho <- rhohat(X, function(x,y){x}) rho plot(rho) Y <- predict(rho) plot(Y) plot(simulate(rho), add=TRUE) # fit <- ppm(X, ~x) rho <- rhohat(fit, "y") opa <- par(mfrow=c(1,2)) plot(predict(rho)) plot(predict(rho, relative=TRUE)) par(opa) plot(predict(rho, what="se"))
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