conddist.saemix(saemix)
conddist.saemix()所属R语言包：saemix
Estimate conditional mean and variance of individual parameters using the MCMC algorithm
有条件的均值和方差的各个参数估计的MCMC算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
When the parameters of the model have been estimated, we can estimate the individual parameters (psi_i).
当模型参数的估计，我们可以估算出个人的参数（psi_i）。
Let hattheta be the estimated value of theta computed with the SAEM algorithm and let p(phi_i |y_i; hattheta) be the conditional distribution of phi_i for 1<=i<=N . We use the MCMC procedure used in the SAEM algorithm to estimate these conditional distributions. We empirically estimate the conditional mean E(phi_i |y_i; hattheta) and the conditional standard deviation sd(phi_i |y_i; hattheta).
，让hattheta的SAEM算法的估计值计算的θ和P（phi_i | y_i，hattheta）的1是的条件分布phi_i，<= I <= N。我们使用SAEM算法使用MCMC程序，估计这些条件分布。我们凭经验估计的条件均值E（phi_i | y_i hattheta）和条件（phi_i y_i，hattheta）的标准偏差SD。
用法----------Usage----------
conddist.saemix(saemixObject,nsamp=1,max.iter=NULL,...)
参数----------Arguments----------
参数：saemixObject
an object returned by the saemix function
返回的对象saemix的函数
参数：nsamp
Number of samples to be drawn in the conditional distribution for each subject. Defaults to 1
要绘制的样品数为每个主题中的条件概率分布。默认为1
参数：max.iter
Maximum number of iterations for the computation of the conditional estimates. Defaults to twice the total number of iterations (sum(saemixObject["options"]$nbiter.saemix)*2)
有条件的估计迭代计算的最大数量。默认值的两倍，总的迭代次数（SUM（saemixObject“选项”] nbiter.saemix $）* 2）
参数：...
optional arguments passed to the plots. Plots will appear if the option displayProgress in the saemixObject object is TRUE
可选参数传递到图表中。绘图时会出现的的选项displayProgress在saemixObject对象是TRUE
Details
详细信息----------Details----------
See PDF documentation for details of the computation. Briefly, the MCMC algorithm is used to obtain samples from the individual conditional distributions of the parameters. The algorithm is initialised for each subject to the conditional estimate of the individual parameters obtained at the end of the SAEMIX fit. A convergence criterion is used to ensure convergence of the mean and variance of the conditional distributions. When nsamp>1, several chains of the MCMC algorithm are run in parallel to obtain samples from the conditional distributions, and the convergence criterion must be achieved for all chains. When nsamp>1, the estimate of the conditional mean is obtained by averaging over the different samples.
查看PDF文档详细计算。简言之，MCMC算法用于从个别的有条件分布的参数以获得样品。该算法被初始化为每个主题的SAEMIX拟合结束时得到的各个参数的条件的估计。甲收敛准则使用，以确保收敛性的有条件分布的平均值和方差。当nsamp> 1时，几个连锁的MCMC算法并行运行，以获得样品的条件分布，和，收敛标准必须达到所有连锁。当条件均值的估计> 1，nsamp是通过以下方式获得平均的不同样品。
The shrinkage for any given parameter for the conditional estimate is obtained as
对于任何给定的参数的条件估计的收缩被获得作为
Sh=1-var(eta_i)/omega(eta)
SH = 1 var（eta_i）的/欧米茄（ETA）
where var(eta_i) is the empirical variance of the estimates of the individual random effects, and omega(eta) is the estimated variance.
其中VAR（eta_i）是经验的个体随机效应的估计方差，和欧米茄（ETA）的估计方差。
The function adds or modifies the following elements in the results:
该函数添加或修改的结果以下因素：
cond.mean.phiConditional mean of the individual distribution of the parameters (obtained as the mean of the samples)
的各个分布的参数（作为样品的平均值获得cond.mean.phiConditional平均值）
cond.var.phiConditional variance of the individual distribution of the parameters (obtained as the mean of the estimated variance of the samples)
的各个分布的参数（作为估计的方差的平均值的样本获得cond.var.phiConditional方差）
cond.shrinkageEstimate of the shrinkage for the conditional estimates
cond.shrinkageEstimate的条件估计的收缩
cond.mean.etaConditional mean of the individual distribution of the parameters (obtained as the mean of the samples)
的各个分布的参数（作为样品的平均值获得cond.mean.etaConditional平均值）
phi.sampAn array with 3 dimensions, giving nsamp samples from the conditional distributions of the individual parameters
phi.sampAn与3维阵列，给予nsamp样品的各个参数的条件分布
phi.samp.varThe estimated individual variances for the sampled parameters phi.samp A warning is output if the maximum number of iterations is reached without convergence (the maximum number of iterations is saemix.options$nbiter.saemix[2]).
phi.samp.varThe估计的个体差异输出的采样的参数phi.samp一个警告是，如果达到最大迭代次数不收敛（最大迭代次数为saemix.options $ nbiter.saemix [2]）。
（作者）----------Author(s)----------
Emmanuelle Comets <emmanuelle.comets@inserm.fr>, Audrey Lavenu, Marc Lavielle.
参考文献----------References----------
Monolix32_UsersGuide.pdf (http://software.monolix.org/sdoms/software/)
参见----------See Also----------
SaemixData,SaemixModel, SaemixObject,saemixControl,saemix
SaemixData，SaemixModel，SaemixObject，saemixControl，saemix
实例----------Examples----------
data(theo.saemix)
saemix.data<-saemixData(name.data=theo.saemix,header=TRUE,sep=" ",na=NA,
name.group=c("Id"),name.predictors=c("Dose","Time"),
name.response=c("Concentration"),name.covariates=c("Weight","Sex"),
units=list(x="hr",y="mg/L",covariates=c("kg","-")), name.X="Time")
model1cpt<-function(psi,id,xidep) {
dose<-xidep[,1]
tim<-xidep[,2]
ka<-psi[id,1]
V<-psi[id,2]
CL<-psi[id,3]
k<-CL/V
ypred<-dose*ka/(V*(ka-k))*(exp(-k*tim)-exp(-ka*tim))
return(ypred)
}
saemix.model<-saemixModel(model=model1cpt,
description="One-compartment model with first-order absorption",
psi0=matrix(c(1.,20,0.5,0.1,0,-0.01),ncol=3, byrow=TRUE,dimnames=list(NULL,
c("ka","V","CL"))),transform.par=c(1,1,1),
covariate.model=matrix(c(0,1,0,0,0,0),ncol=3,byrow=TRUE),fixed.estim=c(1,1,1),
covariance.model=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE),
omega.init=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE), error.model="constant")
saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE)
saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
saemix.fit<-conddist.saemix(saemix.fit,nsamp=3)
# First sample from the conditional distribution [第一个采样的条件分布]
# (a N (nb of subject) by nb.etas (nb of parameters) matrix)[（一）nb.etas N（NB的主题（注意：参数）矩阵）]
saemix.fit["results"]["phi.samp"][,,1]
# Second sample[第二个示例]
saemix.fit["results"]["phi.samp"][,,2]
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 2012-9-29 21:24:14
