R: Maximum-likelihood Fitting of Order Statistics

forder {evd} | R Documentation

## Maximum-likelihood Fitting of Order Statistics

### Description

Maximum-likelihood fitting for the distribution of a selected order statistic of a given number of independent variables from a specified distribution.

### Usage

    forder(x, start, densfun, distnfun, ..., distn, mlen = 1, j = 1,
        largest = TRUE, std.err = TRUE, corr = FALSE, method = "Nelder-Mead")

### Arguments

`x` | A numeric vector.
`start` | A named list giving the initial values for the parameters over which the likelihood is to be maximized.
`densfun, distnfun` | Density and distribution function of the specified distribution.
`...` | Additional parameters, either for the specified distribution or for the optimization function `optim`. If parameters of the distribution are included they will be held fixed at the values given (see Examples). If parameters of the distribution are not included either here or as a named component in `start` they will be held fixed at the default values specified in the corresponding density and distribution functions (assuming they exist; an error will be generated otherwise).
`distn` | A character string, optionally specified as an alternative to `densfun` and `distnfun` such that the density and distribution and functions are formed upon the addition of the prefixes `d` and `p` respectively.
`mlen` | The number of independent variables.
`j` | The order statistic, taken as the `j`th largest (default) or smallest of `mlen`, according to the value of `largest`.
`largest` | Logical; if `TRUE` (default) use the `j`th largest order statistic, otherwise use the `j`th smallest.
`std.err` | Logical; if `TRUE` (the default), the standard errors are returned.
`corr` | Logical; if `TRUE`, the correlation matrix is returned.
`method` | The optimization method (see `optim` for details).

### Details

Maximization of the log-likelihood is performed. The estimated standard errors are taken from the observed information, calculated by a numerical approximation.

If the density and distribution functions are user defined, the order of the arguments must mimic those in R base (i.e. data first, parameters second). Density functions must have `log` arguments.

### Value

Returns an object of class `c("extreme","evd")`. This class is defined in `fextreme`.

The generic accessor functions `fitted` (or `fitted.values`), `std.errors`, `deviance`, `logLik` and `AIC` extract various features of the returned object. The function `anova` compares nested models.

### See Also

`anova.evd`, `fextreme`, `optim`

### Examples

    uvd <- rorder(100, qnorm, mean = 0.56, mlen = 365, j = 2)
    forder(uvd, list(mean = 0, sd = 1), distn = "norm", mlen = 365, j = 2)
    forder(uvd, list(rate = 1), distn = "exp", mlen = 365, j = 2,
      method = "Brent", lower=0.01, upper=10)
    forder(uvd, list(scale = 1), shape = 1, distn = "gamma", mlen = 365, j = 2,
      method = "Brent", lower=0.01, upper=10)
    forder(uvd, list(shape = 1, scale = 1), distn = "gamma", mlen = 365, j = 2)

[Package evd version 2.3-3 Index]