gev {evd} | R Documentation

## The Generalized Extreme Value Distribution

### Description

Density function, distribution function, quantile function and random generation for the generalized extreme value (GEV) distribution with location, scale and shape parameters.

### Usage

    dgev(x, loc=0, scale=1, shape=0, log = FALSE)
    pgev(q, loc=0, scale=1, shape=0, lower.tail = TRUE)
    qgev(p, loc=0, scale=1, shape=0, lower.tail = TRUE)
    rgev(n, loc=0, scale=1, shape=0)

### Arguments

`x, q` | Vector of quantiles.
`p` | Vector of probabilities.
`n` | Number of observations.
`loc, scale, shape` | Location, scale and shape parameters; the `shape` argument cannot be a vector (must have length one).
`log` | Logical; if `TRUE`, the log density is returned.
`lower.tail` | Logical; if `TRUE` (default), probabilities are P[X <= x], otherwise, P[X > x]

### Details

The GEV distribution function with parameters `loc` = a, `scale` = b and `shape` = s is

G(x) = exp[-{1+s(z-a)/b}^(-1/s)]

for 1+s(z-a)/b > 0, where b > 0. If s = 0 the distribution is defined by continuity. If 1+s(z-a)/b <= 0, the value z is either greater than the upper end point (if s < 0), or less than the lower end point (if s > 0).

The parametric form of the GEV encompasses that of the Gumbel, Frechet and reversed Weibull distributions, which are obtained for s = 0, s > 0 and s < 0 respectively. It was first introduced by Jenkinson (1955).

### Value

`dgev` gives the density function, `pgev` gives the distribution function, `qgev` gives the quantile function, and `rgev` generates random deviates.

### References

Jenkinson, A. F. (1955) The frequency distribution of the annual maximum (or minimum) of meteorological elements. Quart. J. R. Met. Soc., 81, 158–171.

### See Also

`fgev`, `rfrechet`, `rgumbel`, `rrweibull`

### Examples

    dgev(2:4, 1, 0.5, 0.8)
    pgev(2:4, 1, 0.5, 0.8)
    qgev(seq(0.9, 0.6, -0.1), 2, 0.5, 0.8)
    rgev(6, 1, 0.5, 0.8)
    p <- (1:9)/10
    pgev(qgev(p, 1, 2, 0.8), 1, 2, 0.8)
    ## [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

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