VGAM (version 1.0-3)

tikuv: Short-tailed Symmetric Distribution Family Function

Description

Fits the short-tailed symmetric distribution of Tiku and Vaughan (1999).

Usage

tikuv(d, lmean = "identitylink", lsigma = "loge", isigma = NULL,
      zero = "sigma")

Arguments

d

The $d$ parameter. It must be a single numeric value less than 2. Then $h=2-d>0$ is another parameter.

lmean, lsigma

Link functions for the mean and standard deviation parameters of the usual univariate normal distribution (see Details below). They are $\mu$ and $\sigma$ respectively. See Links for more choices.

isigma

Optional initial value for $\sigma$. A NULL means a value is computed internally.

zero

A vector specifying which linear/additive predictors are modelled as intercept-only. The values can be from the set {1,2}, corresponding respectively to $\mu$, $\sigma$. If zero = NULL then all linear/additive predictors are modelled as a linear combination of the explanatory variables. For many data sets having zero = 2 is a good idea. 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.

Warning

Under- or over-flow may occur if the data is ill-conditioned, e.g., when $d$ is very close to 2 or approaches -Inf.

Details

The short-tailed symmetric distribution of Tiku and Vaughan (1999) has a probability density function that can be written $$f(y)=\frac{K}{\sqrt{2\pi }\sigma }{[1+\frac{1}{2h}{\left(\frac{y-\mu }{\sigma }\right)}^2]}^2\exp (-\frac{1}{2}(y-\mu {)}^2/{\sigma }^2)$$ where $h=2-d>0$, $K$ is a function of $h$, $-\mathrm{\infty }<y<\mathrm{\infty }$, $\sigma >0$. The mean of $Y$ is $E(Y)=\mu$ and this is returned as the fitted values.

References

Akkaya, A. D. and Tiku, M. L. (2008) Short-tailed distributions and inliers. Test, 17, 282--296.

Tiku, M. L. and Vaughan, D. C. (1999) A family of short-tailed symmetric distributions. Technical report, McMaster University, Canada.

See Also

dtikuv, uninormal.

Examples

# NOT RUN {
m <- 1.0; sigma <- exp(0.5)
tdata <- data.frame(y = rtikuv(n = 1000, d = 1, m = m, s = sigma))
tdata <- transform(tdata, sy = sort(y))
fit <- vglm(y ~ 1, tikuv(d = 1), data = tdata, trace = TRUE)
coef(fit, matrix = TRUE)
(Cfit <- Coef(fit))
with(tdata, mean(y))
# }
# NOT RUN {
 with(tdata, hist(y, prob = TRUE))
lines(dtikuv(sy, d = 1, m = Cfit[1], s = Cfit[2]) ~ sy, data = tdata, col = "orange") 
# }