Diag_Non_Con_Sel: Demonstrate the goodness of fit of the selected non-extreme...
In rjaneUCF/MultiHazard-R-Package: Tools for modeling compound events

Description Usage Arguments Value See Also Examples

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

1 2	Diag_Non_Con_Sel(Data, x_lab = "Data", y_lim_min = 0, y_lim_max = 1, Selected)

`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"`.

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.

Diag_Non_Con

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")