# quantGPD: Fast GPD quantile estimate In brry/extremeStat: Extreme Value Statistics and Quantile Estimation

 quantGPD R Documentation

## Fast GPD quantile estimate

### Description

Fast GPD quantile estimate through L-moments

### Usage

```quantGPD(
x,
probs = c(0.8, 0.9, 0.99),
truncate = 0,
threshold = berryFunctions::quantileMean(x, truncate),
quiet = FALSE,
...
)
```

### Arguments

 `x` Vector with numeric values. NAs are silently ignored. `probs` Probabilities. DEFAULT: c(0.8,0.9,0.99) `truncate, threshold` Truncation proportion or threshold. DEFAULT: 0, computed See `q_gpd`. `addn` Logical: add element with sample size (after truncation). DEFAULT: TRUE `quiet` Should messages from this function be suppressed? DEFAULT: FALSE `...` Further arguments passed to `lmomco::pargpa`

### Value

Vector with quantiles

### Author(s)

Berry Boessenkool, berry-b@gmx.de, Jun 2017

`q_gpd` for a comparison across R packages and methods, `distLquantile` to compare distributions

### Examples

```data(annMax)
quantile(annMax, 0.99)
quantGPD(annMax, 0.99)

## Not run:  # Excluded from CRAN checks to reduce checking time
data(rain, package="ismev") ;  rain <- rain[rain>0]
hist(rain, breaks=50, col=7)
tr <- seq(0,0.999, len=50)
qu <- pbapply::pbsapply(tr, quantGPD, x=rain, probs=c(0.9,0.99,0.999) ) # 30 s
plot(tr, qu[3,], ylim=range(rain), las=1, type="l")
lines(tr, qu[2,], col=2); lines(tr, qu[1,], col=4)

tr <- seq(0.88,0.999, len=50)
qu <- pbapply::pbsapply(tr, quantGPD, x=rain, probs=c(0.9,0.99,0.999) ) # 5 s
plot(tr, qu[3,], ylim=range(rain), las=1, type="l")
lines(tr, qu[2,], col=2); lines(tr, qu[1,], col=4);
tail(qu["n",])

library(microbenchmark)
data(rain, package="ismev"); rain <- rain[rain>0]
mb <- microbenchmark(quantGPD(rain[1:200], truncate=0.8, probs=0.99, addn=F),
distLquantile(rain[1:200], sel="gpa", emp=F, truncate=0.8, quiet=T, probs=0.99)[1,1]
)
boxplot(mb)
# since computing the lmoments takes most of the computational time,
# there's not much to optimize in large samples like n=2000

## End(Not run)

```

brry/extremeStat documentation built on Nov. 24, 2022, 3:35 p.m.