Description Usage Arguments Value Details See also Examples
Fitting the non-stationary index-flood models
1 |
formula |
one or two sided formula describing the model, see Details |
data |
data.frame including the covariate and the extremes to be fitted |
method |
method used for the parameter optimization see ?optim |
tol |
minimum improvement of the log-likelihood between the iteration steps, if the difference between two succesive iteration steps is lower, the iteration ends |
... |
other parameters passed to |
Object of class nim - i.e. simply
The function is a simple wrapper for fitting non-stationary index-flood models with parametric (currently only linear) and smoothing trends in the parameters of the GEV model. Stationary index-flood models can be fitted also. For the data (data.frame
) that are used within the function, the attribute extremes
(see extremes) has to be set. The trend can be either smooth (s) or parametric (p)n.
sample, fit, ...
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data('precip_max')
head(precip_max)
# indicate which columns contain the extremes to be fitted
extremes(precip_max) = 2:ncol(precip_max)
# stationary model
nim( ~1, data = precip_max)
# smooth trend in xi, with bandwidth h = 0.2
nim(xi ~ s(YR, h = 0.2), data = precip_max)
# smooth trend in all parameters, default bandwidth
nim(xi + g + k ~ s(YR), data = precip_max)
# linear trend in xi and gamma
nim(xi + g ~ p(YR), data = precip_max)
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