Migpd_Fit: Fits Multiple independent generalized Pareto models - Fit
In rjaneUCF/MultiHazard-R-Package: Tools for modeling compound events

Description Usage Arguments Value See Also Examples

Fit multiple independent generalized Pareto models to each column of a data frame. Edited version of the migpd function in texmex, to allow for NAs in a time series.

1 2	Migpd_Fit(Data, mth, mqu, penalty = "gaussian", maxit = 10000, trace = 0, verbose = FALSE, priorParameters = NULL)

`Data`	A data frame with `n` columns, each comprising a declustered and if necessary detrended time series to be modelled.
`mth`	Marginal thresholds, above which generalized Pareto models are fitted. Numeric vector of length `n`.
`mqu`	Marginal quantiles, above which generalized Pareto models are fitted. Only one of `mth` and `mqu` should be supplied. Numeric vector of length `n`.
`penalty`	See `ggplot.migpd`.
`maxit`	See `ggplot.migpd`.
`trace`	See `ggplot.migpd`.
`verbose`	See `ggplot.migpd`.
`priorParameters`	See `ggplot.migpd`.

An object of class "migpd". There are coef, print, plot, ggplot and summary functions available.

Decluster Detrend Dataframe_Combine

#With date as first column
S22.GPD<-Migpd_Fit(Data=S22.Detrend.Declustered.df, mqu =c(0.99,0.99,0.99))
#Without date as first column
S22.GPD<-Migpd_Fit(Data=S22.Detrend.Declustered.df[,-1], mqu =c(0.99,0.99,0.99))