Diag_Non_Con_Sel: Demonstrate the goodness of fit of the selected non-extreme...

In rjaneUCF/MultiHazard: Tools for modeling compound events

Demonstrate the goodness of fit of the selected non-extreme marginal distribution

Description

Plots demonstrating the goodness of fit of a selected (not truncated) non-extreme marginal distribution to a dataset.

Usage

Diag_Non_Con_Sel(Data, x_lab = "Data", y_lim_min = 0, y_lim_max = 1,
  Selected)

Arguments

Data	Numeric vector containing realizations of the variable of interest.
x_lab	Numeric vector of length one specifyingLabel on the x-axis of histogram and cummulative distribution plot.
y_lim_min	Numeric vector of length one specifying the lower y-axis limit of the histogram.
y_lim_max	Numeric vector of length one specifying the upper y-axis limit of the histogram.
Selected	Charactor vector of length one specifying the chosen distribution, options are the Gaussian "Gaus" and logistic "Logis".

Value

Panel consisting of three plots. Upper plot: Plots depicting the AIC of the two fitted distributions. Middle plot: Probabilty Density Functions (PDFs) of the selected distribtions superimposed on a histgram of the data. Lower plot: Cummulative distribution function (CDFs) of the selected distribution overlaid on a plot of the empirical CDF.

See Also

Diag_Non_Con

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

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