| LognormalQQ | R Documentation | 
Computes the empirical quantiles of the log-transform of a data vector and the theoretical quantiles of the standard normal distribution. These quantiles are then plotted in a log-normal QQ-plot with the theoretical quantiles on the x-axis and the empirical quantiles on the y-axis.
LognormalQQ(data, plot = TRUE, main = "Log-normal QQ-plot", ...)
data | 
 Vector of   | 
plot | 
 Logical indicating if the quantiles should be plotted in a log-normal QQ-plot, default is   | 
main | 
 Title for the plot, default is   | 
... | 
 Additional arguments for the   | 
By definition, a log-transformed log-normal random variable is normally distributed. We can thus obtain a log-normal QQ-plot from a normal QQ-plot by replacing the empirical quantiles of the data vector by the empirical quantiles from the log-transformed data. We hence plot
(\Phi^{-1}(i/(n+1)), \log(X_{i,n}) )
 for i=1,\ldots,n, where \Phi is the standard normal CDF.
See Section 4.1 of Albrecher et al. (2017) for more details.
A list with following components:
lnqq.the | 
 Vector of the theoretical quantiles from a standard normal distribution.  | 
lnqq.emp | 
 Vector of the empirical quantiles from the log-transformed data.  | 
Tom Reynkens.
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.
ExpQQ, ParetoQQ, WeibullQQ
data(norwegianfire)
# Log-normal QQ-plot for Norwegian Fire Insurance data for claims in 1976.
LognormalQQ(norwegianfire$size[norwegianfire$year==76])
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