psi(saemix)
psi()所属R语言包：saemix
Functions to extract the individual estimates of the parameters and random effects
函数来提取的个别参数估计和随机效应
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These three functions are used to access the estimates of individual parameters and random effects.
这三个功能被用来访问的各个参数的估计和随机效应。
用法----------Usage----------
psi(object, indiv.par)
phi(object, indiv.par)
eta(object, indiv.par)
参数----------Arguments----------
参数：object
an object returned by the saemix function
返回的对象saemix的函数
参数：indiv.par
a string giving the type of estimate to be used (one of "map", for the Maximum A Posteriori estimate, or "eap", for conditional estimate). Defaults to "map"
一个字符串，给出估计使用的类型（一个“图”，最大后验概率估计，或“EAP”，有条件的估计）。默认为“图”
Details
详细信息----------Details----------
The psi_i represent the individual parameter estimates. In the SAEM algorithm, these parameters are assumed to be a transformation of a Gaussian random vector phi_i, where the phi_i can be written as a function of the individual random effects (eta_i), the covariate matrix (C_i) and the vector of fixed effects (mu):
psi_i代表各个参数的估计。在SAEM算法，这些参数被假定为一个高斯随机向量phi_i，其中phi_i可以写成的函数个别随机的效果（eta_i），协变量矩阵（C_i）和向量的固定效应的变换（亩）：
phi_i = C_i mu + eta_i
phi_i = C_i亩+ eta_i
值----------Value----------
These functions are used to access and output the estimates of parameters and random effects. When the object passed to the function does not contain these estimates, they are automatically computed. The object is then returned (invisibly) with these estimates added to the results.
这些功能是用于访问和输出的参数的估计和随机效应。当传递给函数的对象不包含这些估计数字，它们会被自动计算出来的。该对象，然后返回（不可见），这些估计的结果。
（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.options<-list(algorithm=c(1,0,0),seed=632545,save=FALSE,save.graphs=FALSE)
saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)
psi(saemix.fit)
phi(saemix.fit)
eta(saemix.fit,indiv.par="eap")
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。
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发表于 2012-9-29 21:25:51
