Con_Sampling_2D: Conditionally sampling a two-dimensional dataset
In rjaneUCF/MultiHazard-R-Package: Tools for modeling compound events

Description Usage Arguments Value Examples

Creates a data frame where the declustered excesses of a (conditioning) variable are paired with co-occurences of another variable.

1	Con_Sampling_2D(Data_Detrend, Data_Declust, Con_Variable, Thres = 0.97)

`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 `NA`. First object may be a `"Date"` object. Can be `Dataframe_Combine` output.
`Data_Declust`	Data frame containing two (independently) declustered at least partially concurrent time series. Time steps must be equally spaced, with missing values assigned `NA`. Columns must be in the same order as in `Data_Detrend`. First object may be a `"Date"` object. Can be `Dataframe_Combine` output.
`Con_Variable`	Column number (1 or 2) or the column name of the conditioning variable. Default is `1`.
`Thres`	Threshold, as a quantile of the observations of the conditioning variable. Default is `0.97`.

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 name of the conditioning variable.

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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",Thres=0.97)