Con_Sampling_2D_Lag | R Documentation |
Creates a data frame where the declustered excesses of a (conditioning) variable are paired with the maximum value of a second variable over a specified time-lag.
Con_Sampling_2D_Lag(
Data_Detrend,
Data_Declust,
Con_Variable,
u = 0.97,
Thres,
Lag_Backward = 3,
Lag_Forward = 3
)
Data_Detrend |
Data frame containing two at least partially concurrent time series, detrended if necessary. Time steps must be equally spaced, with missing values assigned |
Data_Declust |
Data frame containing two (independently) declustered at least partially concurrent time series. Time steps must be equally spaced, with missing values assigned |
Con_Variable |
Column number (1 or 2) or the column name of the conditioning variable. Default is |
u |
Threshold, as a quantile of the observations of the conditioning variable. Default is |
Thres |
Threshold expressed on the original scale of the observations. Only one of |
Lag_Backward |
Positive lag applied to variable not assigned as the |
Lag_Forward |
Negative lag to variable not assigned as the |
List comprising the specified Threshold
as the quantile of the conditioning variable above which declustered excesses are paired with co-occurences of the other variable, the resulting two-dimensional sample data
and Con_Variable
the name of the conditioning variable. The index of the input dataset that correspond to the events of the conditioning variable x.con
and the non-conditioning variable x.noncon
in the conditonal sample are also provided.
S20.Rainfall<-Con_Sampling_2D(Data_Detrend=S20.Detrend.df[,-c(1,4)],
Data_Declust=S20.Detrend.Declustered.df[,-c(1,4)],
Con_Variable="Rainfall",u=0.97)
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