Given a point process model fitted to a point pattern dataset, this function computes the pseudoscore diagnostic of goodness-of-fit for the model, against moderately clustered or moderately inhibited alternatives of area-interaction type.

```
psstA(object, r = NULL, breaks = NULL, …,
model = NULL,
trend = ~1, interaction = Poisson(),
rbord = reach(interaction), ppmcorrection = "border",
correction = "all",
truecoef = NULL, hi.res = NULL,
nr=spatstat.options("psstA.nr"),
ngrid=spatstat.options("psstA.ngrid"))
```

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 diagnostic should be computed. This argument is usually not specified. There is a sensible default.

breaks

This argument is for internal use only.

…

Extra arguments passed to `quadscheme`

to determine
the quadrature scheme, if `object`

is a point pattern.

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,ppmcorrection`

.

trend,interaction,rbord

ppmcorrection

Optional. Character string specifying the edge correction
for the pseudolikelihood to be used
in fitting the point process model. Passed to `ppm`

.

correction

Optional. Character string specifying which diagnostic quantities
will be computed. Options are `"all"`

and `"best"`

.
The default is to compute all diagnostic quantities.

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.

nr

Optional. Number of `r`

values to be used
if `r`

is not specified.

ngrid

Integer. Number of points in the square grid used to compute the approximate area.

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

),
essentially a data frame of function values.

Columns in this data frame include `dat`

for the pseudosum,
`com`

for the compensator and `res`

for the
pseudoresidual.

There is a plot method for this class. See `fv.object`

.

This computation can take a **very long time**.

To shorten the computation time, choose smaller values of the
arguments `nr`

and `ngrid`

, or reduce the values of their
defaults `spatstat.options("psstA.nr")`

and `spatstat.options("psstA.ngrid")`

.

Computation time is roughly proportional to
`nr * npoints * ngrid^2`

where `npoints`

is the number
of points in the point pattern.

This function computes the pseudoscore test statistic which can be used as a diagnostic for goodness-of-fit of a fitted point process model.

Let \(x\) be a point pattern dataset consisting of points
\(x_1,\ldots,x_n\) in a window \(W\).
Consider a point process model fitted to \(x\), with
conditional intensity
\(\lambda(u,x)\) at location \(u\).
For the purpose of testing goodness-of-fit, we regard the fitted model
as the null hypothesis.
The alternative hypothesis is a family of
hybrid models obtained by combining
the fitted model with the area-interaction process
(see `AreaInter`

). The family of alternatives includes
models that are slightly more regular than the fitted model,
and others that are slightly more clustered than the fitted model.

The pseudoscore, evaluated at the null model, is
$$
V(r) = \sum_i A(x_i, x, r) - \int_W A(u,x, r) \lambda(u,x)
{\rm d} u
$$
where
$$
A(u,x,r) = B(x\cup\{u\},r) - B(x\setminus u, r)
$$
where \(B(x,r)\) is the area of the union of the discs of radius
\(r\) centred at the points of \(x\) (i.e. \(B(x,r)\) is the area
of the dilation of \(x\) by a distance \(r\)).
Thus \(A(u,x,r)\) is the *unclaimed area* associated with
\(u\), that is, the area of that part of the disc
of radius \(r\) centred at the point \(u\) that is
not covered by any of the discs of radius \(r\) centred at
points of \(x\).

According to the Georgii-Nguyen-Zessin formula, \(V(r)\) should have mean zero if the model is correct (ignoring the fact that the parameters of the model have been estimated). Hence \(V(r)\) can be used as a diagnostic for goodness-of-fit.

The diagnostic \(V(r)\) is also called
the **pseudoresidual** of \(S\). On the right
hand side of the equation for \(V(r)\) given above,
the sum over points of \(x\) is called the
**pseudosum** and the integral is called the **pseudocompensator**.

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

Alternative functions:
`psstG`

,
`psst`

,
`Gres`

,
`Kres`

.

Point process models: `ppm`

.

Options: `spatstat.options`

# NOT RUN { pso <- spatstat.options(psstA.ngrid=16,psstA.nr=10) X <- rStrauss(200,0.1,0.05) plot(psstA(X)) plot(psstA(X, interaction=Strauss(0.05))) spatstat.options(pso) # }