Maximum-likelihood fitting for a stationary point process model, over a range of thresholds. Graphs of parameter estimates which aid the selection of a threshold are produced.

`pp.fitrange(data, umin, umax, npy = 365, nint = 10, show = FALSE, ...)`

data

A numeric vector of data to be fitted.

umin, umax

The minimum and maximum thresholds at which the model is fitted.

npy

The number of observations per year/block.

nint

The number of fitted models.

show

Logical; if `TRUE`

, print details of each
fit.

...

Optional arguments to `pp.fit`

.

Three graphs showing maximum likelihood estimates and confidence intervals of the location, scale and shape parameters over a range of thresholds are produced. A list object is returned invisibly with components: 'threshold' numeric vector of length 'nint' giving the thresholds used, 'mle' an 'nint X 3' matrix giving the maximum likelihood parameter estimates (columns are location, scale and shape respectively), 'se' an 'nint X 3' matrix giving the estimated standard errors for the parameter estimates (columns are location, scale and shape, resp.), 'ci.low', 'ci.up' 'nint X 3' matrices giving the lower and upper 95 intervals, resp. (columns same as for 'mle' and 'se').

```
# NOT RUN {
data(rain)
# }
# NOT RUN {
pp.fitrange(rain, 10, 40)
# }
```

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