make.dist(SamplerCompare)
make.dist()所属R语言包：SamplerCompare
Define a probability distribution object
定义一个概率分布对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Defines a probability distribution object for use with
定义的概率分布为使用对象
用法----------Usage----------
log.density=NULL, grad.log.density=NULL,
log.density.and.grad=NULL, initial=NULL,
参数----------Arguments----------
参数：ndim
The size of the distribution's state space.
的分布的状态空间的大小。
参数：name
A human-readable name for the distribution.
人类可读的名称的分配。
参数：name.expression
A name for the distribution in plotmath notation. Used in preference to name in plot functions when available.
分布在plotmath符号的名称。用于name的绘图功能时，可优先。
参数：log.density
A function taking a vector argument that returns the log density of the distribution evaluated at that point.
一个函数的向量参数，返回的记录密度计算分布在这一点上。
参数：grad.log.density
A function taking a vector argument that returns the gradient of the log density of the distribution evaluated at that point.
函数返回log评价在该点的分布密度的梯度的向量参数。
参数：log.density.and.grad
A function taking a vector argument and a logical that returns a list with two elements, log.density and grad.log.density. The logical indicates whether the caller wants the gradient; if not, this function may omit the grad.log.density element in the return value.
一个函数，一个向量参数，并返回一个列表，一个逻辑，有两个元素，log.density和grad.log.density。逻辑指示调用方是否希望梯度，如果没有，这个功能可能会忽略grad.log.density元素的返回值。
参数：initial
A function that returns an overdispersed initial state for an MCMC simulation of this distribution, used by compare.samplers. If unset, uniform draws on a unit hypercube are assumed to be acceptable.
的功能，返回一个overdispersed的初始状态为MCMC模拟的分布，使用compare.samplers。如果没有设置，统一绘制一个单位超立方体被认为是可以接受的。
参数：mean
A vector specifying the true mean of the distribution.
一个向量确定的真实的平均分布。
参数：cov
A matrix specifying the true covariance of the distribution.
真正的协方差矩阵指定的分布。
参数：mean.log.dens
A scalar specifying the true mean of the log density of the distribution. This will depend on the normalization of the log density function.
标量指定的log真实的平均密度的分布。这将取决于log密度函数的归一化。
Details
详细信息----------Details----------
Every distribution must have a name and a dimension. The log density and its gradient are optional; they are used by samplers implemented in R. Samplers implemented in other languages could specifically recognize the name of the distribution instead of calling back into R, though there is a mechanism for C functions to call back. The mean and covariance do not affect sampling, only post-sample diagnostics like autocorrelation time.
每个分配必须有一个名称和尺寸。记录密度及其梯度是可选的;他们所使用的采样器在R.取样器在其他语言中实现的，能特异识别的名称，而不是调用到R的分布实施，但有一个机制的C函数回。的均值和方差不影响采样，样本后诊断，如自相关时间。
For many distributions, it is easier to compute the log density and its gradient at the same time than separately; these will generally specify log.density.and.grad and leave log.density and log.density.and.grad as NULL. The returned object will fill those in with calls to log.density.and.grad. Similarly, if it is simpler to compute them separately, log.density.and.grad will be synthesized from log.density and grad.log.density if necessary.
对于许多发行版，它是在相同的时间比单独计算的log密度和梯度;这些一般会指定log.density.and.grad离开log.density和log.density.and.gradNULL 。返回的对象填补这些在调用log.density.and.grad，。同样的，如果它是简单的，分别计算它们，log.density.and.grad将合成从log.density和grad.log.density如有必要。
mean, cov, and mean.log.dens values are intended to be used by diagnostic routines. mean and mean.log.dens are currently used by compare.samplers when estimating autocorrelation times.
mean，cov，和mean.log.dens值的目的是要使用的诊断例程。 mean和mean.log.dens估计自相关时间时，目前使用compare.samplers。
See make.c.dist for a way to define distributions whose densities are implemented in C instead of R.
make.c.dist的方式来定义分布，其密度是用C实现的，而不是R.
值----------Value----------
A dist object. It has elements with the same names as the arguments to make.dist.
Adist对象。它具有相同名称的参数make.dist。
参见----------See Also----------
compare.samplers, make.c.dist, check.dist.gradient, “R/C Glue in SamplerCompare” (vignette)
compare.samplers，make.c.dist，check.dist.gradient，“R / C胶在SamplerCompare”（暗角）
实例----------Examples----------
# A one dimensional Gamma(3,2) distribution.[一个一维的伽玛（3,2）分布。]
# So that the density does not return NaN outside the support.[使密度不返回NaN以外的支持。]
inflog <- function(x) ifelse(x<=0, -Inf, log(x))
# Define density; unnormalized densities are fine.[定义密度，非标准化的密度都很好。]
gamma32.log.density <- function(x) (3-1)*inflog(x) - x/2
gamma32.grad <- function(x) (3-1)/x - 1/2
# Use make.dist to define the distribution object.[使用make.dist定义分布对象。]
gamma32.dist <- make.dist(1, 'Gamma32', 'plain("Gamma")(3,2)',
log.density=gamma32.log.density,
grad.log.density=gamma32.grad,
mean=3*2, cov=as.matrix(3*2^2))
# Make sure the log density and gradient agree at an arbitrary point.[确保记录密度和梯度同意在任意点。]
check.dist.gradient(gamma32.dist, 17)
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。
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发表于 2012-9-29 21:33:34
