cParetoQQ {ReIns}

Pareto quantile plot for right censored data

Description

Pareto QQ-plot adapted for right censored data.

Usage

cParetoQQ(data, censored, plot = TRUE, main = "Pareto QQ-plot", ...)

Arguments

data

Vector of n observations.

censored

A logical vector of length n indicating if an observation is censored.

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

The Pareto QQ-plot adapted for right censoring is given by

( -\log(1-F_{km}(Z_{j,n})), \log Z_{j,n} )

for j=1,\ldots,n-1, with Z_{i,n} the i-th order statistic of the data and F_{km} the Kaplan-Meier estimator for the CDF. Hence, it has the same empirical quantiles as an ordinary Pareto QQ-plot but replaces the theoretical quantiles -\log(1-j/(n+1)) by -\log(1-F_{km}(Z_{j,n})).

This QQ-plot is only suitable for right censored data, use icParetoQQ for interval censored data.

Value

A list with following components:

pqq.the

Vector of the theoretical quantiles, see Details.

pqq.emp

Vector of the empirical quantiles from the log-transformed data.

Author(s)

Tom Reynkens

References

Beirlant, J., Guillou, A., Dierckx, G. and Fils-Villetard, A. (2007). "Estimation of the Extreme Value Index and Extreme Quantiles Under Random Censoring." Extremes, 10, 151–174.

See Also

ParetoQQ, icParetoQQ, cExpQQ, cLognormalQQ, cWeibullQQ, cHill, KaplanMeier

Examples

# Set seed
set.seed(29072016)

# Pareto random sample
X <- rpareto(500, shape=2)

# Censoring variable
Y <- rpareto(500, shape=1)

# Observed sample
Z <- pmin(X, Y)

# Censoring indicator
censored <- (X>Y)

# Pareto QQ-plot adapted for right censoring
cParetoQQ(Z, censored=censored)