saemix(saemix)
saemix()所属R语言包：saemix
Stochastic Approximation Expectation Maximization (SAEM) algorithm
随机逼近期望最大化（SAEM）算法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
SAEM algorithm perform parameter estimation for nonlinear mixed effects models without any approximation of the model (linearization, quadrature approximation, . . . )
SAEM算法进行非线性混合效应模型的参数估计没有任何近似的模型（线性，正交逼近，...）
用法----------Usage----------
saemix(model, data, control = list())
参数----------Arguments----------
参数：model
an object of class SaemixModel, created by a call to the function saemixModel
一个对象的类SaemixModel中，创造了通过调用函数saemixModel的
参数：data
an object of class SaemixData, created by a call to the function saemixData
一个对象的类SaemixData的，创建通过调用函数saemixData的
参数：control
a list of options, see saemixControl
选项的列表，请参阅saemixControl
Details
详细信息----------Details----------
The SAEM algorithm is a stochastic approximation version of the standard EM algorithm proposed by Kuhn and Lavielle (see reference). Details of the algorithm can be found in the pdf file accompanying the package.
SAEM算法是一种随机近似版本的标准EM算法提出的库恩和Lavielle（请参阅参考资料）。该算法的详情，可发现在pdf文件附带的程序包。
值----------Value----------
An object of class SaemixObject containing the results of the fit of the data by the non-linear mixed effect model. A summary of the results is printed out to the terminal, and, provided the appropriate options have not been changed, numerical and graphical outputs are saved in a directory.
一个对象的类SaemixObject的包含数据的非线性混合效应模型的拟合结果。一个总结的结果是打印出来的终端，并提供相应的选项并没有改变，数字和图形输出保存在一个目录。
（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, plot.saemix
SaemixData，SaemixModel，SaemixObject，saemixControl，plot.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.fit<-saemix(saemix.model,saemix.data,list(seed=632545,directory="newtheo",
save=FALSE,save.graphs=FALSE))
# Prints a summary of the results[打印结果的摘要]
print(saemix.fit)
# Outputs the estimates of individual parameters[输出各个参数的估计]
psi(saemix.fit)
# Shows some diagnostic plots to evaluate the fit[显示一些诊断评估适合的图]
plot(saemix.fit)
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 21:26:42
