R语言 tweedie包 dtweedie.dldphi()函数中文帮助文档(中英文对照)

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dtweedie.dldphi(tweedie)
dtweedie.dldphi()所属R语言包：tweedie

                                        Tweedie Distributions
                                         特威迪分派

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Derivatives of the log-likelihood with respect to phi
衍生工具的对数似然尊重phi的


用法----------Usage----------


dtweedie.dldphi(phi, mu, power, y )
dtweedie.dldphi.saddle(phi, mu, power, y )



参数----------Arguments----------

参数：y
vector of quantiles
向量的位数


参数：mu
the mean
的意思


参数：phi
the dispersion
分散


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。


值----------Value----------

the value of the derivative  d(l)/d(phi) where l is the log-likelihood for the specified Tweedie distribution. dtweedie.dldphi.saddle uses the saddlepoint approximation to determine the derivative; dtweedie.dldphi uses an infinite series expansion.
价值的衍生d(l)/d(phi)其中l是对数似然为指定的Tweedie分布。 dtweedie.dldphi.saddle使用鞍点近似，以确定该衍生工具; dtweedie.dldphi使用一个无穷级数扩张。


（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&ndash;86.
Series evaluation of Tweedie exponential dispersion model densities Statistics and Computing, 15(4). 267&ndash;280.
Tweedie family densities: methods of evaluation. Proceedings of the 16th International Workshop on Statistical Modelling, Odense, Denmark, 2&ndash;6 July
Exponential dispersion models. Journal of the Royal Statistical Society, B, 49, 127&ndash;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&ndash;529.
A user-friendly extrapolation method for oscillatory infinite integrals. Mathematics of Computation 51(183), 249&ndash;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.saddle,  dtweedie, tweedie.profile,  tweedie
dtweedie.saddle，dtweedie，tweedie.profile，tweedie


实例----------Examples----------


### Plot dl/dphi against candidate values of phi[＃＃图升/ DPHI对候选值披]
power <- 2
mu <- 1
phi <- seq(2, 8, by=0.1)

set.seed(10000) # For reproducability[为了再现性]
y <- rtweedie( 100, mu=mu, power=power, phi=3)
   # So we expect the maximum to occur at  phi=3[因此，我们希望最大限度地出现在φ= 3]

dldphi <- dldphi.saddle <- array( dim=length(phi))

for (i in (1:length(phi))) {
   dldphi[i] <- dtweedie.dldphi( y=y, power=power, mu=mu, phi=phi[i])
   dldphi.saddle[i] <- dtweedie.dldphi.saddle( y=y, power=power, mu=mu, phi=phi[i])
}

plot( dldphi ~ phi, lwd=2, type="l",
   ylab=expression(phi), xlab=expression(paste("dl / d",phi) ) )
lines( dldphi.saddle ~ phi, lwd=2, col=2, lty=2)
legend( "bottomright", lwd=c(2,2), lty=c(1,2), col=c(1,2),
   legend=c("'Exact' (using series)","Saddlepoint") )

# Neither are very good in this case![在这种情况下，也不是很不错的！]
   

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