Description Usage Arguments Details Value Author(s) References See Also Examples

Given a point process model fitted to a point pattern dataset,
this function computes the residual *G* function,
which serves as a diagnostic for goodness-of-fit of the model.

1 |

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

`...` |
Arguments passed to |

This command provides a diagnostic for the goodness-of-fit of
a point process model fitted to a point pattern dataset.
It computes a residual version of the *G* function of the
dataset, which should be approximately zero if the model is a good
fit to the data.

In normal use, `object`

is a fitted point process model
or a point pattern. Then `Gres`

first calls `Gcom`

to compute both the nonparametric estimate of the *G* function
and its model compensator. Then `Gres`

computes the
difference between them, which is the residual *G*-function.

Alternatively, `object`

may be a function value table
(object of class `"fv"`

) that was returned by
a previous call to `Gcom`

. Then `Gres`

computes the
residual from this object.

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`

.

and Jesper Moller.

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:
`Gcom`

,
`Gest`

.

Alternative functions:
`Kres`

,
`psstA`

,
`psstG`

,
`psst`

.

Model-fitting:
`ppm`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
data(cells)
fit0 <- ppm(cells, ~1) # uniform Poisson
G0 <- Gres(fit0)
plot(G0)
# Hanisch correction estimate
plot(G0, hres ~ r)
# uniform Poisson is clearly not correct
fit1 <- ppm(cells, ~1, Strauss(0.08))
plot(Gres(fit1), hres ~ r)
# fit looks approximately OK; try adjusting interaction distance
plot(Gres(cells, interaction=Strauss(0.12)))
# How to make envelopes
## Not run:
E <- envelope(fit1, Gres, model=fit1, nsim=39)
plot(E)
## End(Not run)
# For computational efficiency
Gc <- Gcom(fit1)
G1 <- Gres(Gc)
``` |

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