posterior.fit(MSBVAR)
posterior.fit()所属R语言包：MSBVAR
Estimates the marginal likelihood or log posterior probability for
估计边际的可能性或登录后验概率
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes the marginal log likelihood other posterior fit measures for BVAR, BSVAR, and MSBVAR models fit with szbvar, szbsvar and, msbvar (and their posterior samplers).
计算边际对数似然后合适的措施BVAR，BSVAR，和MSBVAR模型适合与szbvar，szbsvar，msbvar（后取样）。
用法----------Usage----------
posterior.fit(varobj, A0.posterior.obj=NULL, maxiterbs=500)
参数----------Arguments----------
参数：varobj
Varies for BVAR, BSVAR, or MSBVAR models. For a BVAR model, varobj = output from a call to szbvar. For a BSVAR model, varobj = output from a call to szbsvar. For MSBVAR models, varobj = output from a call to gibbs.msbvar.
而异，BVAR，BSVAR，或MSBVAR模型。对于一个BVAR模型，varobj=调用szbvar的输出。对于一个BSVAR模型，varobj=调用szbsvar的输出。对于MSBVAR模型，varobj=调用gibbs.msbvar的输出。
参数：A0.posterior.obj
MCMC Gibbs object for the B-SVAR model A(0) from gibbs.A0
MCMC吉布斯对象的B-SVAR模型A(0)gibbs.A0
参数：maxiterbs
Number of iterations for the bridge sampler for computing the marginal likelihood for MSBVAR models
数的桥梁采样迭代的边缘似然MSBVAR模型的计算
Details
详细信息----------Details----------
Estimates the marginal log likelihood, also known as a log marginal data density for the various models. For the BVAR models, this can be computed in closed form. For the BSVAR models the MCMC data augmentation method of Chib (1995) is employed. For the MSBVAR models, the importance sampler, reciprocal importance sampler, and bridge sampler methods of Fruwirth-Schnatter (2006) are used. Consult these references for details (or look at the source code).
估计边缘的对数似然，也被称为一个log边际各种型号的数据密度。对于BVAR模型，这可以在封闭的形式计算。对于BSVAR模型的的MCMC数据增强了较大的（1995）的方法。对于MSBVAR模型，的重要性采样器，互惠的重要性采样，和桥采样方法的Fruwirth Schnatter（2006）。咨询的详细信息，这些引用（或查看源代码）。
The computations are done using compiled C++ and Fortran code as of version 0.3.0. See the package source code for details about the implementation.
计算使用编译的C + +和Fortran代码版本0.3.0。即将实施的详细信息，请参阅程序包源代码。
值----------Value----------
BVAR:
BVAR：
A list of the class "posterior.fit.VAR" that includes the following elements:
，一个列表类的“posterior.fit.VAR”，包括以下元素：
参数：data.marg.llf
Log marginal density, the probability of the data after integrating out the parameters in the model.
登录边际密度，积分后的模型中的参数的数据的概率。
参数：data.marg.post
Predictive marginal posterior density
预测后验边缘密度
参数：coefficient.post
Contribution to the posterior fit from the pdf of the coefficients.
后适合的PDF系数的贡献。
BSVAR:
BSVAR：
A list of the class "posterior.fit.BSVAR" that includes the following elements:
，一个列表类的“posterior.fit.BSVAR”，包括以下元素：
参数：log.prior
Log prior probability
登录先验概率
参数：log.llf
T x 1 list of the log probabilities for each observation conditional on the parameters.
T x 1的log列表中每个条件的参数的观察概率。
参数：log.posterior.Aplus
Log marginal probability of A(1),...,A(p) conditional on the data and A(0)
登录边际概率A(1),...,A(p)条件对数据和A(0)
参数：log.marginal.data.density
Log data density or marginal log likelihood, the probability of the data after integrating out the parameters in the model.
登录后整合模型中的参数数据密度或边缘对数似然概率的数据。
参数：log.marginal.A0k
m x 1 list of the log probabilities of each column (corresponding to the equations) of A(0) conditional on the other columns.
m x 1log概率A(0)条件的其他列中的每一列（对应于方程）列表。
MSBVAR:
MSBVAR：
A list of the class "posterior.fit.MSBVAR" that includes the following elements:
，一个列表类的“posterior.fit.MSBVAR”，包括以下元素：
注意----------Note----------
The log Bayes factor for two model can be computed using the log.marginal.data.density:
log贝叶斯因子为两个模型，可以计算使用log.marginal.data.density：
log BF = log.marginal.data.density.1 - log.marginal.data.density.2
登录BF = log.marginal.data.density.1 - log.marginal.data.density.2
Note that at present, the scale factors for the BVAR and B-SVAR models are different (one used the concentrated likelihood, the other does NOT). Thus, one cannot compare fit measures across the two functions. To compare a recursive B-SVAR to a non-recursive B-SVAR model, one should estimate the recursive model with szbsvar using the appropriate ident matrix and then call posterior.fit on the two B-SVAR models!
需要注意的是，目前，BVAR和B-SVAR模型的比例因子是不同的（一个用于集中的可能性，其他没有）。因此，人们不能跨越两个函数比较合适的措施。比较递归的B-的SVAR到一个非递归的B-SVAR模型，估计与szbsvar使用适当的ident矩阵的递归模型，然后调用“posterior.fit了两个B SVAR模型！
（作者）----------Author(s)----------
Patrick T. Brandt and W. Ryan Davis
参考文献----------References----------
Journal of the American Statistical Association. 90(432): 1313–1321.
structural vector autoregressions" Journal of Economic Dynamics \& Control. 28:349–366.
Models. Springer Series in Statistics New York: Springer., esp. Sections 5.4 and 5.5.
参见----------See Also----------
szbvar, szbsvar, gibbs.A0, gibbs.msbvar, and print.posterior.fit for a print method.
szbvar，szbsvar，gibbs.A0，gibbs.msbvar和print.posterior.fit打印方法。
实例----------Examples----------
## Not run: [＃不运行：]
varobj <- szbsvar(Y, p, z = NULL, lambda0, lambda1, lambda3, lambda4,
lambda5, mu5, mu6, ident, qm = 4)
A0.posterior <- gibbs.A0(varobj, N1, N2)
fit <- posterior.fit(varobj, A0.posterior)
print(fit)
## End(Not run)[＃（不执行）]
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。
|
发表于 2012-9-23 15:42:44
