View source: R/Diag_Non_Con_Trunc.R
Diag_Non_Con_Trunc | R Documentation |
Fits ten (truncated) non-extreme marginal distributions to a dataset and returns three plots demonstrating their relative goodness of fit.
The distributions are the Birnbaum-Saunders "BS"
, exponential "Exp"
, two-parameter gamma "Gam(2)"
, three-parameter gamma "Gam(3)"
, mixed two-parameter gamma "GamMix(2)"
, mixed three-parameter gamma "GamMix(3)"
, lognormal "LNorm"
, truncated normal "TNorm"
, Tweedie "Twe"
and the Weibull "Weib"
.
Diag_Non_Con_Trunc(
Data,
Omit = NA,
x_lab = "Data",
y_lim_min = 0,
y_lim_max = 1
)
Data |
Numeric vector containing realizations of the variable of interest. |
Omit |
Character vector specifying any distributions that are not to be tested. Default |
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 |
y_lim_max |
Numeric vector of length one specifying the upper y-axis limit of the histogram. Default is |
Dataframe $AIC
giving the AIC associated with each distribution and the name of the best fitting distribution $Best_fit
. Panel consisting of three plots. Upper plot: Plot depicting the AIC of the ten fitted distributions. Middle plot: Probability Density Functions (PDFs) of the fitted distributions superimposed on a histogram of the data. Lower plot: Cumulative Distribution Functions (CDFs) of the fitted distributions overlaid on a plot of the empirical CDF.
Copula_Threshold_2D
S20.OsWL<-Con_Sampling_2D(Data_Detrend=S20.Detrend.df[,-c(1,4)],
Data_Declust=S20.Detrend.Declustered.df[,-c(1,4)],
Con_Variable="OsWL",Thres=0.97)
Diag_Non_Con_Trunc(Data=S20.OsWL$Data$Rainfall,x_lab="Rainfall (Inches)",
y_lim_min=0,y_lim_max=2)
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