SpliceQQ: Splicing quantile plot

View source: R/Splicing_plots.R

SpliceQQR Documentation

Splicing quantile plot


Computes the empirical quantiles of a data vector and the theoretical quantiles of the fitted spliced distribution. These quantiles are then plotted in a splicing QQ-plot with the theoretical quantiles on the x-axis and the empirical quantiles on the y-axis.


SpliceQQ(X, splicefit, p = NULL, plot = TRUE, main = "Splicing QQ-plot", ...)



Vector of n observations.


A SpliceFit object, e.g. output from SpliceFitPareto or SpliceFitGPD.


Vector of probabilities used in the QQ-plot. If NULL, the default, we take p equal to 1/(n+1),...,n/(n+1).


Logical indicating if the quantiles should be plotted in a splicing QQ-plot, default is TRUE.


Title for the plot, default is "Splicing QQ-plot".


Additional arguments for the plot function, see plot for more details.


This QQ-plot is given by

(Q(p_j), \hat{Q}(p_j)),

for j=1,\ldots,n where Q is the quantile function of the fitted splicing model and \hat{Q} is the empirical quantile function and p_j=j/(n+1).

See Reynkens et al. (2017) and Section 4.3.1 in Albrecher et al. (2017) for more details.


A list with following components:


Vector of the theoretical quantiles of the fitted spliced distribution.


Vector of the empirical quantiles from the data.


Tom Reynkens


Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.

Reynkens, T., Verbelen, R., Beirlant, J. and Antonio, K. (2017). "Modelling Censored Losses Using Splicing: a Global Fit Strategy With Mixed Erlang and Extreme Value Distributions". Insurance: Mathematics and Economics, 77, 65–77.

Verbelen, R., Gong, L., Antonio, K., Badescu, A. and Lin, S. (2015). "Fitting Mixtures of Erlangs to Censored and Truncated Data Using the EM Algorithm." Astin Bulletin, 45, 729–758

See Also

SpliceQQ_TB, qSplice, SpliceFitPareto, SpliceFitGPD, SpliceECDF, SpliceLL, SplicePP


## Not run: 

# Pareto random sample
X <- rpareto(1000, shape = 2)

# Splice ME and Pareto
splicefit <- SpliceFitPareto(X, 0.6)

x <- seq(0, 20, 0.01)

# Plot of spliced CDF
plot(x, pSplice(x, splicefit), type="l", xlab="x", ylab="F(x)")

# Plot of spliced PDF
plot(x, dSplice(x, splicefit), type="l", xlab="x", ylab="f(x)")

# Fitted survival function and empirical survival function 
SpliceECDF(x, X, splicefit)

# Log-log plot with empirical survival function and fitted survival function
SpliceLL(x, X, splicefit)

# PP-plot of empirical survival function and fitted survival function
SplicePP(X, splicefit)

# PP-plot of empirical survival function and 
# fitted survival function with log-scales
SplicePP(X, splicefit, log=TRUE)

# Splicing QQ-plot
SpliceQQ(X, splicefit)

## End(Not run)

ReIns documentation built on Sept. 14, 2023, 1:09 a.m.