VGAM (version 0.7-10)

weibull: Weibull Distribution Family Function

Description

Maximum likelihood estimation of the 2-parameter Weibull distribution. No observations should be censored.

Usage

weibull(lshape = "loge", lscale = "loge", 
        eshape = list(), escale = list(),
        ishape = NULL,   iscale = NULL,
        nrfs = 1, imethod=1, zero = 2)

Value

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

Warning

This function is under development to handle other censoring situations. The version of this function which will handle censored data will be called cenweibull(). It is currently being written and will use SurvS4 as input. It should be released in later versions of VGAM.

If the shape parameter is less than two then misleading inference may result, e.g., in the summary and vcov of the object.

Details

The Weibull density for a response $Y$ is $$f(y;a,b) = a y^{a-1} \exp[-(y/b)^a] / (b^a)$$ for $a > 0$, $b > 0$, $y > 0$. The cumulative distribution function is $$F(y;a,b) = 1 - \exp[-(y/b)^a].$$ The mean of $Y$ is $b \, \Gamma(1+ 1/a)$ (returned as the fitted values), and the mode is at $b\,(1-1/a)^{1/a}$ when $a>1$. The density is unbounded for $a<1$. the="" $k$th="" moment="" about="" origin="" is="" $e(y^k)="b^k" \,="" \gamma(1+="" k="" a)$.="" hazard="" function="" $a="" t^{a-1}="" b^a$.<="" p="">

This VGAM family function currently does not handle censored data. Fisher scoring is used to estimate the two parameters. Although the Fisher information matrices used here are valid in all regions of the parameter space, the regularity conditions for maximum likelihood estimation are satisfied only if $a>2$ (according to Kleiber and Kotz (2003)). If this is violated then a warning message is issued. One can enforce $a>2$ by choosing lshape = "logoff" and eshape=list(offset=-2).

References

Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, Hoboken, NJ: Wiley-Interscience.

Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1994) Continuous Univariate Distributions, 2nd edition, Volume 1, New York: Wiley.

Gupta, R. D. and Kundu, D. (2006) On the comparison of Fisher information of the Weibull and GE distributions, Journal of Statistical Planning and Inference, 136, 3130--3144.

See Also

dweibull, gev, lognormal, expexp.

Examples

# Complete data
x = runif(n <- 1000)
y = rweibull(n, shape=exp(1+x), scale = exp(-0.5))
fit = vglm(y ~ x, weibull, trace=TRUE)
coef(fit, mat=TRUE)
vcov(fit)
summary(fit)