# plot.bermantest

##### Plot Result of Berman Test

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

##### Usage

```
# S3 method for bermantest
plot(x, ...,
lwd=par("lwd"), col=par("col"), lty=par("lty"),
lwd0=lwd, col0=2, lty0=2)
```

##### Arguments

- x
Object to be plotted. An object of class

`"bermantest"`

produced by`berman.test`

.- …
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 curve.

- col0,lwd0,lty0
The width, colour and type of lines used to plot the predicted (null) distribution curve.

##### 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 `berman.test`

.

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

was computed using
`berman.test( ,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
`berman.test( ,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

```
# NOT RUN {
# 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 <- berman.test(fit0, xcoord, "Z1")
# plot result of test
plot(k, col="red", col0="green")
# Z2 test
k2 <- berman.test(fit0, xcoord, "Z2")
plot(k2, col="red", col0="green")
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*