Fitting the Point Process Model Over a Range of Thresholds
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').
pp.fit, mrl.plot, gpd.fit, gpd.fitrange
## Not run: data(rain) ## Not run: pp.fitrange(rain, 10, 40)