Diag_Non_Con: Goodness of fit of non-extreme marginal distributions

In rjaneUCF/MultiHazard: Tools for modeling compound events

Goodness of fit of non-extreme marginal distributions

Description

Fits two (unbounded) non-extreme marginal distributions to a dataset and returns three plots demonstrating their relative goodness of fit.

Usage

Diag_Non_Con(Data, x_lab, y_lim_min = 0, y_lim_max = 1)

Arguments

Data	Numeric vector containing realizations of the variable of interest.
x_lab	Character vector of length one specifying the label on the x-axis of histogram and cumulative distribution plot.
y_lim_min	Numeric vector of length one specifying the lower y-axis limit of the histogram. Default is 0.
y_lim_max	Numeric vector of length one specifying the upper y-axis limit of the histogram. Default is 1.

Value

Dataframe $AIC giving the AIC associated with each distribution and the name of the best fitting distribution $Best_fit. Panel consisting of three plots. Upper plot: Plot depicting the AIC of the two fitted distributions. Middle plot: Probability Density Functions (PDFs) of the fitted distributions superimposed on a histogram of the data. Lower plot: Cumulative Distribution Functions (CDFs) of the fitted distributions overlaid on a plot of the empirical CDF.

See Also

Copula_Threshold_2D

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)
Diag_Non_Con(Data=S20.Rainfall$Data$OsWL,x_lab="O-sWL (ft NGVD 29)",
             y_lim_min=0,y_lim_max=1.5)

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