Fitting the GPD Model Over a Range of Thresholds
Maximum-likelihood fitting for a stationary GPD model, over a range of thresholds. Graphs of parameter estimates which aid the selection of a threshold are produced.
gpd.fitrange(data, umin, umax, 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. |
nint |
The number of fitted models. |
show |
Logical; if |
... |
Optional arguments to |
Two graphs showing maximum likelihood estimates and confidence intervals of the shape and modified scale 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: gpd.fitrange(rain, 10, 40)
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