Con_Sampling_2D: Conditionally sampling a two-dimensional dataset

In rjaneUCF/MultiHazard: Tools for modeling compound events

Conditionally sampling a two-dimensional dataset

Description

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

Usage

Con_Sampling_2D(Data_Detrend, Data_Declust, Con_Variable, u = 0.97, Thres = NA)

Arguments

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.
u	Threshold, as a quantile of the observations of the conditioning variable. Default is 0.97.
Thres	Threshold expressed on the original scale of the observations. Only one of u and Thres should be supplied. Default is NA.

Value

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. The index of the input dataset that correspond to the events in the conditonal sample x.con are also provided.

Examples

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)

rjaneUCF/MultiHazard documentation built on April 20, 2024, 12:48 a.m.