Computes the empirical quantiles of the log-transform of a data vector and the theoretical quantiles of the standard exponential distribution. These quantiles are then plotted in a Pareto QQ-plot with the theoretical quantiles on the x-axis and the empirical quantiles on the y-axis.
ParetoQQ(data, plot =TRUE, main ="Pareto QQ-plot",...)
Arguments
data: Vector of n observations.
plot: Logical indicating if the quantiles should be plotted in a Pareto QQ-plot, default is TRUE.
main: Title for the plot, default is "Pareto QQ-plot".
...: Additional arguments for the plot function, see plot for more details.
Details
It can be easily seen that a log-transformed Pareto random variable is exponentially distributed. We can hence obtain a Pareto QQ-plot from an exponential QQ-plot by replacing the empirical quantiles from the data vector by the empirical quantiles from the log-transformed data. We hence plot
(−log(1−i/(n+1)),logXi,n)
for i=1,...,n,
with Xi,n the i-th order statistic of the data.
See Section 4.1 of Albrecher et al. (2017) for more details.
Returns
A list with following components: - pqq.the: Vector of the theoretical quantiles from a standard exponential distribution.
pqq.emp: Vector of the empirical quantiles from the log-transformed data.
References
Albrecher, H., Beirlant, J. and Teugels, J. (2017). Reinsurance: Actuarial and Statistical Aspects, Wiley, Chichester.
Beirlant J., Goegebeur Y., Segers, J. and Teugels, J. (2004). Statistics of Extremes: Theory and Applications, Wiley Series in Probability, Wiley, Chichester.
Author(s)
Tom Reynkens based on S-Plus code from Yuri Goegebeur.
data(norwegianfire)# Exponential QQ-plot for Norwegian Fire Insurance data for claims in 1976.ExpQQ(norwegianfire$size[norwegianfire$year==76])# Pareto QQ-plot for Norwegian Fire Insurance data for claims in 1976.ParetoQQ(norwegianfire$size[norwegianfire$year==76])