R语言 saemix包 saemix.plot.select()函数中文帮助文档(中英文对照)

loveR

saemix.plot.select(saemix)
saemix.plot.select()所属R语言包：saemix

                                         Plots of the results obtained by SAEM
                                         图SAEM得到的结果

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Several plots (selectable by the type argument) are currently available: convergence plot, individual plots, predictions versus observations, distribution plots, residual plots, VPC.
一些图（可选择的类型参数）是目前可供选择：收敛的图，个别图，预测与观察，分布图，残差图，VPC。


用法----------Usage----------


saemix.plot.select(saemixObject, data = FALSE, convergence = FALSE,
  likelihood = FALSE, individual.fit = FALSE, population.fit = FALSE,
  both.fit = FALSE, observations.vs.predictions = FALSE,
  residuals.scatter = FALSE, residuals.distribution = FALSE,
  random.effects = FALSE, correlations = FALSE,
  parameters.vs.covariates = FALSE, randeff.vs.covariates = FALSE,
  marginal.distribution = FALSE, vpc = FALSE, npde = FALSE, ...)



参数----------Arguments----------

参数：saemixObject
an object returned by the saemix function
返回的对象saemix的函数


参数：data
if TRUE, produce a plot of the data. Defaults to FALSE
如果为TRUE，产生的数据曲线。默认为false


参数：convergence
if TRUE, produce a convergence plot. Defaults to FALSE
如果为TRUE，产生一个收敛图。默认为false


参数：likelihood
if TRUE, produce a plot of the estimation of the LL by importance sampling. Defaults to FALSE
如果为TRUE，生产的LL重要性抽样估计的图。默认为false


参数：individual.fit
if TRUE, produce individual fits with individual estimates. Defaults to FALSE
如果为TRUE，个人适合个人估计。默认为false


参数：population.fit
if TRUE, produce individual fits with population estimates. Defaults to FALSE
如果为TRUE，产生个人的配合与人口估计数字。默认为false


参数：both.fit
if TRUE, produce individual fits with both individual and population estimates. Defaults to FALSE
如果为TRUE，个人适合个人和人口估计数字。默认为false


参数：observations.vs.predictions
if TRUE, produce a plot of observations versus predictions. Defaults to FALSE
如果为TRUE，产生的观测与预测的图。默认为false


参数：residuals.scatter
if TRUE, produce scatterplots of residuals versus predictor and predictions. Defaults to FALSE
如果为TRUE，预测和预测残差与散点图。默认为false


参数：residuals.distribution
if TRUE, produce plots of the distribution of residuals. Defaults to FALSE
如果为TRUE，产生的残差分布图。默认为false


参数：random.effects
if TRUE, produce boxplots of the random effects. Defaults to FALSE
如果为TRUE，产生的随机效应的盒形图。默认为false


参数：correlations
if TRUE, produce a matrix plot showing the correlation between random effects. Defaults to FALSE
如果为TRUE，产生一个矩阵图显示随机效应之间的相关性。默认为false


参数：parameters.vs.covariates
if TRUE, produce plots of the relationships between parameters and covariates, using the Empirical Bayes Estimates of individual parameters. Defaults to FALSE
如果为TRUE，生产参数和协变量之间的关系图，使用的各个参数的经验Bayes估计。默认为false


参数：randeff.vs.covariates
if TRUE, produce plots of the relationships between random effects and covariates, using the Empirical Bayes Estimates of individual random effects. Defaults to FALSE
如果为TRUE，产生随机效应和协变量之间的关系图，使用的经验贝叶斯估计的个体随机效应。默认为false


参数：marginal.distribution
if TRUE, produce plots of the marginal distribution of the random effects. Defaults to FALSE
如果为TRUE，产生的随机效应的边际分布图。默认为false


参数：vpc
if TRUE, produce Visual Predictive Check plots. Defaults to FALSE
如果为TRUE，产生视觉的预测检查图。默认为false


参数：npde
if TRUE, produce plots of the npde. Defaults to FALSE
如果为TRUE，生产图的npde。默认为false


参数：...
optional arguments passed to the plots
可选参数传递给该图


Details

详细信息----------Details----------

This function plots different graphs related to the algorithm (convergence plots, likelihood estimation) as well as diagnostic graphs. A description is provided in the PDF documentation.   
这个函数绘制不同的图形相关的算法（收敛的图，似然估计），以及诊断图表。说明中提供的PDF文档。

dataA spaghetti plot of the data, displaying the observed data y as a function of the regression variable (eg time for a PK application)
DATAA意大利面条的数据曲线，显示所观察到的数据y作为回归变量的函数（例如，时间为一个PK应用程序）

convergenceFor each parameter in the model, this plot shows the evolution of the parameter estimate versus the iteration number
convergenceFor模型中的每个参数，此图显示的参数估计与迭代次数的演变

likelihoodEstimation of the likelihood estimated by importance sampling, as a function of the number of MCMC samples
likelihoodEstimation重要性采样估计的可能性，作为MCMC中的样本的数目的函数

individual.fitIndividual fits, using the individual parameters with the individual covariates
individual.fitIndividual配合，个人与个人的协变量的参数

population.fitIndividual fits, using the population parameters with the individual covariates
population.fitIndividual配合，使用人口参数与个人的协变量

both.fitIndividual fits, using the population parameters with the individual covariates and the individual parameters with the individual covariates
both.fitIndividual适合，使用的人口参数与个人的协变量的协变量和各个参数

observations.vs.predictionsPlot of the predictions computed with the population parameters versus the observations (left), and plot of the predictions computed with the individual parameters versus the observations (right)
observations.vs.predictionsPlot的意见（左）与总体参数的计算，预测和图的各个参数与观测计算与预测（右）

residuals.scatterScatterplot of standardised residuals versus the X predictor and versus predictions. These plots are shown for individual and population residuals, as well as for npde when they are available
residuals.scatterScatterplot与在X的预测和对预测残差的标准化。它们可用时，这些图中示出用于个人和人口的残差，以及为npde

residuals.distributionDistribution of standardised residuals, using histograms and QQ-plot. These plots are shown for individual and population residuals, as well as for npde when they are available
residuals.distributionDistribution的标准化残差，使用直方图和QQ图。它们可用时，这些图中示出用于个人和人口的残差，以及为npde

random.effectsBoxplot of the random effects
random.effectsBoxplot的随机效应

correlationsCorrelation between the random effects, with a smoothing spline
correlationsCorrelation之间的随机效果，与平滑曲线

parameters.versus.covariatesPlots of the estimate of the individual parameters versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
parameters.versus.covariatesPlots的各个参数的估计与协变量，使用连续的变量散点图，盒形图分类协变量

randeff.versus.covariatesPlots of the estimate of the individual random effects versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
randeff.versus.covariatesPlots的个体随机效应的估计与协变量，使用连续的变量散点图，盒形图分类协变量

marginal.distributionDistribution of each parameter in the model (conditional on covariates when some are included in the model)
marginal.distributionDistribution的每个模型中的参数（有条件的协变量时，一些包含在模型中）

npdePlot of npde as in package npde
npde在包npde，npdePlot的

vpcVisual Predictive Check   
vpcVisual预测检查


值----------Value----------

None
无


（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, default.saemix.plots,
SaemixObject，saemix，default.saemix.plots，


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.plot.select(saemix.fit,data=TRUE,main="Spaghetti plot of data")

# Putting several graphs on the same plot[把几个图形相同的图]
par(mfrow=c(2,2))
saemix.plot.select(saemix.fit,data=TRUE,vpc=TRUE,
  observations.vs.predictions=TRUE, new=FALSE)

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
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