# plot.bermantest

##### Plot Result of Berman Test

Plot the result of Berman's test of goodness-of-fit

##### Usage

```
## S3 method for class 'bermantest':
plot(x, ...,
lwd=par("lwd"), col=par("col"), lty=par("lty"),
lwd0=lwd, col0=col, lty0=lty)
```

##### Arguments

- x
- Object to be plotted. An object of class
`"bermantest"`

produced by`bermantest`

. - ...
- extra arguments that will be passed to the plotting function
`plot.ecdf`

. - col,lwd,lty
- The width, colour and type of lines used to plot the empirical distribution.
- col0,lwd0,lty0
- The width, colour and type of lines used to plot the predicted distribution.

##### Details

This is the `plot`

method for the class `"bermantest"`

.
An object of this class represents the outcome of Berman's test
of goodness-of-fit of a spatial Poisson point process model,
computed by `bermantest`

.

For the *Z1* test (i.e. if `x`

was computed using
`bermantest( ,which="Z1")`

),
the plot displays the two cumulative distribution functions
that are compared by the test: namely the empirical cumulative distribution
function of the covariate at the data points, $\hat F$,
and the predicted
cumulative distribution function of the covariate under the model,
$F_0$, both plotted against the value of the covariate.
Two vertical lines show the mean values of these two distributions.
If the model is correct, the two curves should be close; the test is
based on comparing the two vertical lines.

For the *Z2* test (i.e. if `x`

was computed using
`bermantest( ,which="Z2")`

), the plot displays the empirical
cumulative distribution function of the values
$U_i = F_0(Y_i)$ where $Y_i$ is the
value of the covariate at the $i$-th data point. The diagonal line
with equation $y=x$ is also shown. Two vertical lines show the
mean of the values $U_i$ and the value $1/2$. If the
model is correct, the two curves should be close. The test is based on
comparing the two vertical lines.

##### Value

`NULL`

.

##### See Also

##### Examples

```
# synthetic data: nonuniform Poisson process
X <- rpoispp(function(x,y) { 100 * exp(-x) }, win=square(1))
# fit uniform Poisson process
fit0 <- ppm(X, ~1)
# test covariate = x coordinate
xcoord <- function(x,y) { x }
# test wrong model
k <- bermantest(fit0, xcoord, "Z1")
# plot result of test
plot(k, col="red", col0="green")
```

*Documentation reproduced from package spatstat, version 1.16-2, License: GPL (>= 2)*