gumbelII {VGAM} | R Documentation

## Gumbel-II Distribution Family Function

### Description

Maximum likelihood estimation of the 2-parameter Gumbel-II distribution.

### Usage

    gumbelII(lscale = "loge", lshape = "loge", iscale = NULL, ishape = NULL,
             probs.y = c(0.2, 0.5, 0.8), perc.out = NULL, imethod = 1,
             zero = "shape", nowarning = FALSE)

### Arguments

`nowarning` | Logical. Suppress a warning?
`lshape, lscale` | Parameter link functions applied to the (positive) shape parameter (called s below) and (positive) scale parameter (called b below). See `Links` for more choices.

Parameter link functions applied to the

`ishape, iscale` | Optional initial values for the shape and scale parameters.
`imethod` | See `weibullR`.
`zero, probs.y` | Details at `CommonVGAMffArguments`.
`perc.out` | If the fitted values are to be quantiles then set this argument to be the percentiles of these, e.g., 50 for median.

### Details

The Gumbel-II density for a response Y is

f(y;b,s) = s y^(s-1) * exp(-(y/b)^s) / [b^s]

for b > 0, s > 0, y > 0. The cumulative distribution function is

F(y;b,s) = exp(-(y/b)^(-s)).

The mean of Y is b * gamma(1 - 1/s) (returned as the fitted values) when s>1, and the variance is b^2 * Gamma(1-2/s) when s>2. This distribution looks similar to `weibullR`, and is due to Gumbel (1954).

This VGAM family function currently does not handle censored data. Fisher scoring is used to estimate the two parameters. Probably similar regularity conditions hold for this distribution compared to the Weibull distribution.

### Value

An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm`, and `vgam`.

### Note

See `weibullR`. This VGAM family function handles multiple responses.

### Author(s)

T. W. Yee

### References

Gumbel, E. J. (1954). Statistical theory of extreme values and some practical applications. Applied Mathematics Series, volume 33, U.S. Department of Commerce, National Bureau of Standards, USA.

### See Also

`dgumbelII`, `gumbel`, `gev`.

### Examples

    gdata <- data.frame(x2 = runif(nn <- 1000))
    gdata <- transform(gdata, heta1  = +1,
                              heta2  = -1 + 0.1 * x2,
                              ceta1 =  0,
                              ceta2 =  1)
    gdata <- transform(gdata, shape1 = exp(heta1),
                              shape2 = exp(heta2),
                              scale1 = exp(ceta1),
                              scale2 = exp(ceta2))
    gdata <- transform(gdata,
                       y1 = rgumbelII(nn, scale = scale1, shape = shape1),
                       y2 = rgumbelII(nn, scale = scale2, shape = shape2))

    fit <- vglm(cbind(y1, y2) ~ x2,
                gumbelII(zero = c(1, 2, 3)), data = gdata, trace = TRUE)
    coef(fit, matrix = TRUE)
    vcov(fit)
    summary(fit)

[Package VGAM version 1.0-3 Index]