Given a point process model fitted to a point pattern dataset,
this function computes the *compensator*
of the \(K\) function based on the fitted model
(as well as the usual nonparametric estimates
of \(K\) based on the data alone).
Comparison between the nonparametric and model-compensated \(K\)
functions serves as a diagnostic for the model.

```
Kcom(object, r = NULL, breaks = NULL, ...,
correction = c("border", "isotropic", "translate"),
conditional = !is.poisson(object),
restrict = FALSE,
model = NULL,
trend = ~1, interaction = Poisson(), rbord = reach(interaction),
compute.var = TRUE,
truecoef = NULL, hi.res = NULL)
```

object

Object to be analysed.
Either a fitted point process model (object of class `"ppm"`

)
or a point pattern (object of class `"ppp"`

)
or quadrature scheme (object of class `"quad"`

).

r

Optional. Vector of values of the argument \(r\) at which the function \(K(r)\) should be computed. This argument is usually not specified. There is a sensible default.

breaks

This argument is for advanced use only.

…

Ignored.

correction

Optional vector of character strings specifying the edge
correction(s) to be used. See `Kest`

for options.

conditional

Optional. Logical value indicating whether to compute the estimates for the conditional case. See Details.

restrict

Logical value indicating
whether to compute the restriction estimator (`restrict=TRUE`

) or
the reweighting estimator (`restrict=FALSE`

, the default).
Applies only if `conditional=TRUE`

.
See Details.

model

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

) to be re-fitted to the data
using `update.ppm`

, if `object`

is a point pattern.
Overrides the arguments `trend,interaction,rbord`

.

trend,interaction,rbord

compute.var

Logical value indicating whether to compute the Poincare variance bound for the residual \(K\) function (calculation is only implemented for the isotropic correction).

truecoef

Optional. Numeric vector. If present, this will be treated as
if it were the true coefficient vector of the point process model,
in calculating the diagnostic. Incompatible with `hi.res`

.

hi.res

Optional. List of parameters passed to `quadscheme`

.
If this argument is present, the model will be
re-fitted at high resolution as specified by these parameters.
The coefficients
of the resulting fitted model will be taken as the true coefficients.
Then the diagnostic will be computed for the default
quadrature scheme, but using the high resolution coefficients.

A function value table (object of class `"fv"`

),
essentially a data frame of function values.
There is a plot method for this class. See `fv.object`

.

This command provides a diagnostic for the goodness-of-fit of
a point process model fitted to a point pattern dataset.
It computes an estimate of the \(K\) function of the
dataset, together with a *model compensator* of the
\(K\) function, which should be approximately equal if the model is a good
fit to the data.

The first argument, `object`

, is usually a fitted point process model
(object of class `"ppm"`

), obtained from the
model-fitting function `ppm`

.

For convenience, `object`

can also be a point pattern
(object of class `"ppp"`

). In that case, a point process
model will be fitted to it, by calling `ppm`

using the arguments
`trend`

(for the first order trend),
`interaction`

(for the interpoint interaction)
and `rbord`

(for the erosion distance in the border correction
for the pseudolikelihood). See `ppm`

for details
of these arguments.

The algorithm first extracts the original point pattern dataset
(to which the model was fitted) and computes the
standard nonparametric estimates of the \(K\) function.
It then also computes the *model compensator* of the
\(K\) function. The different function estimates are returned
as columns in a data frame (of class `"fv"`

).

The argument `correction`

determines the edge correction(s)
to be applied. See `Kest`

for explanation of the principle
of edge corrections. The following table gives the options
for the `correction`

argument, and the corresponding
column names in the result:

`correction` |
description of correction |
nonparametric |
compensator |

`"isotropic"` |
Ripley isotropic correction | `iso` |
`icom` |

`"translate"` |
Ohser-Stoyan translation correction | `trans` |
`tcom` |

The nonparametric estimates can all be expressed in the form $$ \hat K(r) = \sum_i \sum_{j < i} e(x_i,x_j,r,x) I\{ d(x_i,x_j) \le r \} $$ where \(x_i\) is the \(i\)-th data point, \(d(x_i,x_j)\) is the distance between \(x_i\) and \(x_j\), and \(e(x_i,x_j,r,x)\) is a term that serves to correct edge effects and to re-normalise the sum. The corresponding model compensator is $$ {\bf C} \, \tilde K(r) = \int_W \lambda(u,x) \sum_j e(u,x_j,r,x \cup u) I\{ d(u,x_j) \le r\} $$ where the integral is over all locations \(u\) in the observation window, \(\lambda(u,x)\) denotes the conditional intensity of the model at the location \(u\), and \(x \cup u\) denotes the data point pattern \(x\) augmented by adding the extra point \(u\).

If the fitted model is a Poisson point process, then the formulae above are exactly what is computed. If the fitted model is not Poisson, the formulae above are modified slightly to handle edge effects.

The modification is determined by the arguments
`conditional`

and `restrict`

.
The value of `conditional`

defaults to `FALSE`

for Poisson models
and `TRUE`

for non-Poisson models.
If `conditional=FALSE`

then the formulae above are not modified.
If `conditional=TRUE`

, then the algorithm calculates
the *restriction estimator* if `restrict=TRUE`

,
and calculates the *reweighting estimator* if `restrict=FALSE`

.
See Appendix D of Baddeley, Rubak and Moller (2011).
Thus, by default, the reweighting estimator is computed
for non-Poisson models.

The nonparametric estimates of \(K(r)\) are approximately unbiased
estimates of the \(K\)-function, assuming the point process is
stationary. The model compensators are unbiased estimates
*of the mean values of the corresponding nonparametric estimates*,
assuming the model is true. Thus, if the model is a good fit, the mean value
of the difference between the nonparametric estimates and model compensators
is approximately zero.

Baddeley, A., Rubak, E. and Moller, J. (2011)
Score, pseudo-score and residual
diagnostics for spatial point process models.
*Statistical Science* **26**, 613--646.

Related functions:
`Kres`

,
`Kest`

.

Alternative functions:
`Gcom`

,
`psstG`

, `psstA`

, `psst`

.

Point process models: `ppm`

.

```
# NOT RUN {
fit0 <- ppm(cells, ~1) # uniform Poisson
# }
# NOT RUN {
if(interactive()) {
plot(Kcom(fit0))
# compare the isotropic-correction estimates
plot(Kcom(fit0), cbind(iso, icom) ~ r)
# uniform Poisson is clearly not correct
}
fit1 <- ppm(cells, ~1, Strauss(0.08))
# }
# NOT RUN {
K1 <- Kcom(fit1)
K1
if(interactive()) {
plot(K1)
plot(K1, cbind(iso, icom) ~ r)
plot(K1, cbind(trans, tcom) ~ r)
# how to plot the difference between nonparametric estimates and compensators
plot(K1, iso - icom ~ r)
# fit looks approximately OK; try adjusting interaction distance
}
fit2 <- ppm(cells, ~1, Strauss(0.12))
# }
# NOT RUN {
K2 <- Kcom(fit2)
if(interactive()) {
plot(K2)
plot(K2, cbind(iso, icom) ~ r)
plot(K2, iso - icom ~ r)
}
# }
```

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