Compute the trend or intensity of a fitted point process model as a function of one of its covariates.

`effectfun(model, covname, …, se.fit=FALSE, nvalues=256)`

model

A fitted point process model (object of class
`"ppm"`

, `"kppm"`

, `"lppm"`

, `"dppm"`

, `"rppm"`

or `"profilepl"`

).

covname

The name of the covariate. A character string. (Needed only if the model has more than one covariate.)

…

The fixed values of other covariates (in the form
`name=value`

) if required.

se.fit

Logical. If `TRUE`

, asymptotic standard errors of the estimates
will be computed, together with a 95% confidence interval.

nvalues

Integer. The number of values of the covariate (if it is numeric) for which the effect function should be evaluated. We recommend at least 256.

A data frame containing a column of values of the covariate and a column
of values of the fitted trend.
If `se.fit=TRUE`

, there are 3 additional columns containing the
standard error and the upper and lower limits of a confidence interval.

If the covariate named `covname`

is numeric (rather than a factor
or logical variable), the return value is
also of class `"fv"`

so that it can be plotted immediately.

For a Poisson point process model, the trend is the same as the
intensity of the point process. For a more general Gibbs model, the trend
is the first order potential in the model (the first order term in the
Gibbs representation). In Poisson or Gibbs models fitted by
`ppm`

, the trend is the only part of the model that
depends on the covariates.

The function `dppm`

which fits
a determinantal point process model allows the user to specify the
intensity `lambda`

. In such cases the effect function is
undefined, and `effectfun`

stops with an error message.

The object `model`

should be an object of class
`"ppm"`

, `"kppm"`

, `"lppm"`

, `"dppm"`

, `"rppm"`

or `"profilepl"`

representing a point process model fitted to point pattern data.

The model's trend formula should involve a spatial covariate
named `covname`

. This could be `"x"`

or `"y"`

representing one of the Cartesian coordinates.
More commonly the covariate
is another, external variable that was supplied when fitting the model.

The command `effectfun`

computes the fitted trend
of the point process `model`

as a function of the covariate
named `covname`

.
The return value can be plotted immediately, giving a
plot of the fitted trend against the value of the covariate.

If the model also involves covariates other than `covname`

,
then these covariates will be held fixed. Values for
these other covariates must be provided as arguments
to `effectfun`

in the form `name=value`

.

If `se.fit=TRUE`

, the algorithm also calculates
the asymptotic standard error of the fitted trend,
and a (pointwise) asymptotic 95% confidence interval for the
true trend.

This command is just a wrapper for the prediction method
`predict.ppm`

. For more complicated computations
about the fitted intensity, use `predict.ppm`

.

# NOT RUN { X <- copper$SouthPoints D <- distfun(copper$SouthLines) fit <- ppm(X ~ polynom(D, 5)) effectfun(fit) plot(effectfun(fit, se.fit=TRUE)) fitx <- ppm(X ~ x + polynom(D, 5)) plot(effectfun(fitx, "D", x=20)) # }