DIC(SpatialExtremes)
DIC()所属R语言包：SpatialExtremes
Deviance Information Criterion
越轨信息准则
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function computes the Deviance Information Criterion (DIC), and related quantities, which is a hierarchical modeling generalization of the Akaike Information Criterion. It is useful for Bayesian model selection.
此函数计算的越轨行为信息准则（DIC），相关的数量，这是一个层次化的建模的赤池信息准则的推广。它是有用的贝叶斯模型选择。
用法----------Usage----------
DIC(object, ..., fun = "mean")
参数----------Arguments----------
参数：object
An object of class latent — typically this will be the output of latent.
对象的类latent - 通常这将是输出latent。
参数：...
Optional arguments. Not implemented.
可选参数。未实现。
参数：fun
A chararcter string giving the name of the function to be used to summarize the Markov chain. The default is to consider the posterior mean.
一个chararcter字符串，给出的函数的名称被用来总结的马尔可夫链。默认值是考虑后的平均值。
Details
详细信息----------Details----------
The deviance is
越轨行为是
where y are the data, θ are the unknown parameters of the models and p(y | θ) is the likelihood function. Thus the expected deviance, a measure of how well the model fits the data, is given by
y的数据，θ是未知参数的模型和p(y | θ)是似然函数。因此，预期的偏差，衡量的模型拟合数据的，
while the effective number of parameters is
而有效的参数的数量是
where θ^* is point estimate of the posterior distribution, e.g., the posterior mean. Finally the DIC is given by
θ^*是点估计的后验分布，例如，后的平均值。最后由下式给出的DIC
In accordance with the AIC, models with smaller DIC should be preferred to models with larger DIC. Roughly speaking, differences of more than 10 might rule out the model with the higher DIC, differences between 5 and 10 are substantial.
按照与AIC较小的DIC，模型应该是首选的机型较大的DIC。粗略地讲，超过10的差异可能会排除该模型具有较高的DIC，在5和10之间的差异是巨大的。
值----------Value----------
A vector of length 3 that returns the DIC, effective number of parameters eNoP and an estimate of the expected deviance
长度为3的向量，返回DIC，有效的数字参数eNoP和预期偏差的估计
（作者）----------Author(s)----------
Mathieu Ribatet
参考文献----------References----------
A. (2002) Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B 64, 583–639.
参见----------See Also----------
AIC
AIC
实例----------Examples----------
## Generate realizations from the model[＃从模型生成实现]
n.site <- 15
n.obs <- 35
coord <- cbind(lon = runif(n.site, -10, 10), lat = runif(n.site, -10 , 10))
gp.loc <- rgp(1, coord, "powexp", sill = 4, range = 20, smooth = 1)
gp.scale <- rgp(1, coord, "powexp", sill = 0.4, range = 5, smooth = 1)
gp.shape <- rgp(1, coord, "powexp", sill = 0.01, range = 10, smooth = 1)
locs <- 26 + 0.5 * coord[,"lon"] + gp.loc
scales <- 10 + 0.2 * coord[,"lat"] + gp.scale
shapes <- 0.15 + gp.shape
data <- matrix(NA, n.obs, n.site)
for (i in 1:n.site)
data[,i] <- rgev(n.obs, locs[i], scales[i], shapes[i])
loc.form <- y ~ lon
scale.form <- y ~ lat
shape.form <- y ~ 1
hyper <- list()
hyper$sills <- list(loc = c(1,8), scale = c(1,1), shape = c(1,0.02))
hyper$ranges <- list(loc = c(2,20), scale = c(1,5), shape = c(1, 10))
hyper$smooths <- list(loc = c(1,1/3), scale = c(1,1/3), shape = c(1, 1/3))
hyper$betaMeans <- list(loc = rep(0, 2), scale = c(9, 0), shape = 0)
hyper$betaIcov <- list(loc = solve(diag(c(400, 100))),
scale = solve(diag(c(400, 100))),
shape = solve(diag(c(10), 1, 1)))
## We will use an exponential covariance function so the jump sizes for[＃我们将使用指数的协方差函数，所以跳的大小为]
## the shape parameter of the covariance function are null.[＃协方差函数的形状参数是空的。]
prop <- list(gev = c(2.5, 1.5, 0.3), ranges = c(40, 20, 20), smooths = c(0,0,0))
start <- list(sills = c(4, .36, 0.009), ranges = c(24, 17, 16), smooths
= c(1, 1, 1), beta = list(loc = c(26, 0), scale = c(10, 0),
shape = c(0.15)))
mc <- latent(data, coord, loc.form = loc.form, scale.form = scale.form,
shape.form = shape.form, hyper = hyper, prop = prop, start = start,
n = 500)
DIC(mc)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-30 12:41:20
