Migpd_Fit | R Documentation |
Fit multiple independent generalized Pareto models to each column of a data frame. Edited version of the migpd
function in texmex
, to allow for NA
s in a time series.
Migpd_Fit(
Data,
Data_Full = NA,
mth,
mqu,
penalty = "gaussian",
maxit = 10000,
trace = 0,
verbose = FALSE,
priorParameters = NULL
)
Data |
A data frame with |
Data_Full |
A data frame with |
mth |
Marginal thresholds, above which generalized Pareto models are fitted. Numeric vector of length |
mqu |
Marginal quantiles, above which generalized Pareto models are fitted. Only one of |
penalty |
See |
maxit |
See |
trace |
See |
verbose |
See |
priorParameters |
See |
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))
#Same GPDs fit as above but thresholds given on the original scale
S22.Rainfall.Quantile<-quantile(na.omit(S22.Detrend.Declustered.df$Rainfall),0.99)
S22.OsWL.Quantile<-quantile(na.omit(S22.Detrend.Declustered.df$OsWL),0.99)
S22.GW.Quantile<-quantile(na.omit(S22.Detrend.Declustered.df$Groundwater),0.99)
S22.GPD<-Migpd_Fit(Data=S22.Detrend.Declustered.df[,-1],
mqu =c(S22.Rainfall.Quantile,S22.OsWL.Quantile,S22.GW.Quantile))
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