fim.saemix(saemix)
fim.saemix()所属R语言包：saemix
Computes the Fisher Information Matrix by linearisation
计算Fisher信息矩阵的线性化
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimate by linearisation the Fisher Information Matrix and the standard error of the estimated parameters.
线性Fisher信息矩阵和参数估计值的标准误差的估计。
用法----------Usage----------
参数----------Arguments----------
参数：saemixObject
an object returned by the saemix function
返回的对象saemix的函数
Details
详细信息----------Details----------
The inverse of the Fisher Information Matrix provides an estimate of the variance of the estimated parameters theta. This matrix cannot be computed in closed-form for nonlinear mixed-effect models; instead, an approximation is obtained as the Fisher Information Matrix of the Gaussian model deduced from the nonlinear mixed effects model after linearisation of the function f around the conditional expectation of the individual Gaussian parameters. This matrix is a block matrix (no correlations between the estimated fixed effects and the estimated variances).
Fisher信息矩阵的逆提供了一个估算的方差估计参数θ波。这个矩阵可以不计算在封闭形式的非线性混合效应模型;代替，一个近似的得到的费希尔信息矩阵的高斯模型推导的非线性混合效应模型的线性函数f后，周围的条件期望的个人高斯参数。这个矩阵是分块矩阵（估计固定效应估计方差之间无相关性）。
值----------Value----------
The function returns an updated version of the object saemix.fit in which the following elements have been added:
该函数返回一个更新版本的对象saemix.fit以下元素已添加：
se.fixed:standard error of fixed effects, obtained as part of the diagonal of the inverse of the Fisher Information Matrix (only when fim.saemix has been run, or when the saemix.options$algorithms[2] is 1)
se.fixed：标准误差的固定效应，作为Fisher信息矩阵（仅当fim.saemix已经运行时，或者当在saemix.options $算法[2]为1的逆对角线的一部分）得到
se.omega2:standard error of the variance of random effects, obtained as part of the diagonal of the inverse of the Fisher Information Matrix (only when fim.saemix has been run, or when the saemix.options$algorithms[2] is 1)
se.omega2：标准误差的方差的随机效应，作为对角线的Fisher信息矩阵（仅当fim.saemix已经运行时，或者当逆的一部分得到的saemix.options $算法[2]是1 ）
se.res:standard error of the parameters of the residual error model, obtained as part of the diagonal of the inverse of the Fisher Information Matrix (only when fim.saemix has been run, or when the saemix.options$algorithms[2] is 1)
se.res：标准误差的残余误差的模型，作为部分的对角线的Fisher信息矩阵的逆（仅当fim.saemix已经运行，获得的参数，或当saemix.options $算法[2]是1）
fim:Fisher Information Matrix
FIM：Fisher信息矩阵
ll.lin: likelihood calculated by linearisation
ll.lin计算的线性化的可能性
（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----------
SaemixObject,saemix
SaemixObject，saemix
实例----------Examples----------
# Running the main algorithm to estimate the population parameters[运行的主要算法来估计总体参数]
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(algorithm=c(1,0,0),seed=632545,save=FALSE,save.graphs=FALSE)
saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
# Estimating the Fisher Information Matrix using the result of saemix [估计Fisher信息矩阵使用saemix的结果，]
# & returning the result in the same object[返回的结果在同一个对象]
fim.saemix(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 21:24:38
