tweedie.dev(tweedie)
tweedie.dev()所属R语言包：tweedie
Tweedie Distributions: the deviance function
特威迪分布：越轨行为功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The deviance function for the Tweedie family of distributions
越轨功能的泰迪家族的分布
用法----------Usage----------
tweedie.dev(y, mu, power)
参数----------Arguments----------
参数：y
vector of quantiles (which can be zero if 1 < power < 2
向量的位数（可以是零，如果1 < power < 2
参数：mu
the mean
的意思
参数：power
the value of power such that the variance is var(Y) = phi * mu^power
值的方差是power等var(Y) = phi * mu^power
Details
详细信息----------Details----------
The Tweedie family of distributions belong to the class of exponential dispersion models (<acronym>EDM</acronym>s), famous for their role in generalized linear models. The Tweedie distributions are the <acronym>EDM</acronym>s with a variance of the form var(Y) = phi * mu^power where power is greater than or equal to one, or less than or equal to zero. This function only evaluates for power greater than or equal to one. Special cases include the normal (power=0), Poisson (power=1 with phi=1), gamma (power=2) and inverse Gaussian (power=3) distributions. For other values of power, the distributions are still defined but cannot be written in closed form, and hence evaluation is very difficult.
泰迪家族的分布，属于类指数扩散模型（<acronym> EDM </首字母缩写），著名发挥的作用，广义线性模型中。 Tweedie爵士分布<acronym>EDM</的缩写> s的形式的方差var(Y) = phi * mu^power其中power是大于或等于1，或小于或等于零。此功能只计算power大于或等于1。特殊情况包括正常（power=0），泊松（power=1phi=1），γ（power=2）和逆高斯（power=3）分布。对于其他power的值，分布仍定义但不能被写入在封闭的形式，因此，评价是非常困难的。
The deviance is defined by deviance as “up to a constant, minus twice the maximized log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero.”
越轨行为的定义是deviance为“为一个常数，减去两倍的最大化log可能性。在合理的情况下，恒定的选择，使饱和模型有偏差为零。“
值----------Value----------
the value of the deviance for the given Tweedie distribution with parameters mu, phi and power.
Tweedie分布参数为给定的值的偏差行为mu，phi和power。
（作者）----------Author(s)----------
Peter Dunn (<a href="mailto:pdunn2@usc.edu.au">pdunn2@usc.edu.au</a>)
参考文献----------References----------
Evaluation of Tweedie exponential dispersion model densities by Fourier inversion. Statistics and Computing, 18, 73–86.
Series evaluation of Tweedie exponential dispersion model densities Statistics and Computing, 15(4). 267–280.
Tweedie family densities: methods of evaluation. Proceedings of the 16th International Workshop on Statistical Modelling, Odense, Denmark, 2–6 July
Exponential dispersion models. Journal of the Royal Statistical Society, B, 49, 127–162.
Theory of Dispersion Models. Chapman and Hall, London.
The numerical evaluation of very oscillatory infinite integrals by extrapolation. Mathematics of Computation 38(158), 517–529.
A user-friendly extrapolation method for oscillatory infinite integrals. Mathematics of Computation 51(183), 249–266.
An index which distinguishes between some important exponential families. Statistics: Applications and New Directions. Proceedings of the Indian Statistical Institute Golden Jubilee International Conference (Eds. J. K. Ghosh and J. Roy), pp. 579-604. Calcutta: Indian Statistical Institute.
参见----------See Also----------
dtweedie, dtweedie.saddle, tweedie, deviance, glm
dtweedie，dtweedie.saddle，tweedie，deviance，glm
实例----------Examples----------
### Plot a Tweedie deviance function when 1<p<2[＃＃图一特威迪越轨行为的功能，当1 <p <2时]
mu <- 1
y <- seq(0, 6, length=100)
dev1 <- tweedie.dev( y=y, mu=mu, power=1.1)
dev2 <- tweedie.dev( y=y, mu=mu, power=1.5)
dev3 <- tweedie.dev( y=y, mu=mu, power=1.9)
plot(range(y), range( c(dev1, dev2, dev3)),
type="n", lwd=2, ylab="Deviance", xlab=expression(italic(y)) )
lines( y, dev1, lty=1, col=1, lwd=2 )
lines( y, dev2, lty=2, col=2, lwd=2 )
lines( y, dev3, lty=3, col=3, lwd=2 )
legend("top", col=c(1,2,3), lwd=c(2,2,2), lty=c(1,2,3),
legend=c("p=1.1","p=1.5", "p=1.9") )
### Plot a Tweedie deviance function when p>2[＃＃图Tweedie爵士偏差函数，当p> 2]
mu <- 1
y <- seq(0.1, 6, length=100)
dev1 <- tweedie.dev( y=y, mu=mu, power=2) # Gamma[伽玛]
dev2 <- tweedie.dev( y=y, mu=mu, power=3) # Inverse Gaussian[逆高斯]
dev3 <- tweedie.dev( y=y, mu=mu, power=4)
plot(range(y), range( c(dev1, dev2, dev3)),
type="n", lwd=2, ylab="Deviance", xlab=expression(italic(y)) )
lines( y, dev1, lty=1, col=1, lwd=2 )
lines( y, dev2, lty=2, col=2, lwd=2 )
lines( y, dev3, lty=3, col=3, lwd=2 )
legend("top", col=c(1,2,3), lwd=c(2,2,2), lty=c(1,2,3),
legend=c("p=2 (gamma)", "p=3 (inverse Gaussian)", "p=4") )
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注：
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发表于 2012-10-1 13:08:53
