VGAM (version 0.9-4)

exponential: Exponential Distribution

Description

Maximum likelihood estimation for the exponential distribution.

Usage

exponential(link = "loge", location = 0, expected = TRUE,
            shrinkage.init = 0.95, zero = NULL)

Arguments

link

Parameter link function applied to the positive parameter $rate$. See Links for more choices.

location

Numeric of length 1, the known location parameter, $A$, say.

expected

Logical. If TRUE Fisher scoring is used, otherwise Newton-Raphson. The latter is usually faster.

shrinkage.init, zero

See CommonVGAMffArguments for information.

Value

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

Details

The family function assumes the response $Y$ has density $$f(y) = \lambda \exp(-\lambda (y-A))$$ for $y > A$, where $A$ is the known location parameter. By default, $A=0$. Then $E(Y) = A + 1/ \lambda$ and $Var(Y) = 1/ \lambda^2$.

References

Forbes, C., Evans, M., Hastings, N. and Peacock, B. (2011) Statistical Distributions, Hoboken, NJ, USA: John Wiley and Sons, Fourth edition.

See Also

amlexponential, laplace, expgeometric, explogff, poissonff, mix2exp, freund61, simulate.vlm.

Examples

edata <- data.frame(x2 = runif(nn <- 100) - 0.5)
edata <- transform(edata, x3 = runif(nn) - 0.5)
edata <- transform(edata, eta = 0.2 - 0.7 * x2 + 1.9 * x3)
edata <- transform(edata, rate = exp(eta))
edata <- transform(edata, y = rexp(nn, rate = rate))
with(edata, stem(y))

fit.slow <- vglm(y ~ x2 + x3, exponential, data = edata, trace = TRUE, crit = "c")
fit.fast <- vglm(y ~ x2 + x3, exponential(exp = FALSE), data = edata,
                 trace = TRUE, crit = "coef")
coef(fit.slow, mat = TRUE)
summary(fit.slow)