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

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

                                         Functions implementing each type of plot in SAEM
                                         功能实现每种类型的图在SAEM

                                         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, VPC, residual plots.
几个图（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，VPC，残差图。


用法----------Usage----------


saemix.plot.data(saemixObject, ...)
saemix.plot.convergence(saemixObject,niter=0, ...)
saemix.plot.llis(saemixObject, ...)
saemix.plot.obsvspred(saemixObject, ...)
saemix.plot.distribresiduals(saemixObject, ...)
saemix.plot.scatterresiduals(saemixObject, ...)
saemix.plot.fits(saemixObject, ...)
saemix.plot.distpsi(saemixObject, ...)
saemix.plot.randeff(saemixObject, ...)
saemix.plot.correlations(saemixObject, ...)
saemix.plot.parcov(saemixObject, ...)
saemix.plot.randeffcov(saemixObject, ...)
saemix.plot.npde(saemixObject, ...)
saemix.plot.vpc(saemixObject,npc = FALSE, ...)

saemix.plot.parcov.aux(saemixObject, partype = "p", ...)
compute.sres(saemixObject)
compute.eta.map(saemixObject)



参数----------Arguments----------

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


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


参数：npc
for VPC, computes Numerical Predictive Checks (currently not implemented)
为VPC，计算数值预报的检查（目前尚未实现）


参数：niter
the convergence plots are shown up to iteration "niter". Defaults to 0, which indicates the convergence plots should be plotted up to the maximal number of iterations set for the algorithm
的收敛曲线显示了迭代“硝石”。默认为0，这表示的收敛图应被绘制的算法设置的最大数目的迭代


参数：partype
(this function is not user-level)
（此功能是用户级）


Details

详细信息----------Details----------

These functions implement plots different graphs related to the algorithm (convergence plots, likelihood estimation) as well as diagnostic graphs. A description is provided in the PDF documentation.
这些函数实现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。

saemix.plot.parcov.aux, compute.sres and compute.eta.map are helper functions, not intended to be called by the user directly.
saemix.plot.parcov.aux，compute.sres和compute.eta.map的辅助功能，不是要由用户直接调用。

By default, the following plots are produced:   
默认情况下，下面的图是：

saemix.plot.data:A spaghetti plot of the data, displaying the observed data y as a function of the regression variable (time for a PK application)
saemix.plot.data：意大利面条的数据曲线，作为回归变量的函数（一个PK应用程序的时间显示所观察到的数据y）

saemix.plot.convergence:For each parameter in the model, this plot shows the evolution of the parameter estimate versus the iteration number
saemix.plot.convergence：对于每一个模型中的参数，此图显示的参数估计与迭代次数的演变

saemix.plot.llis:Graph showing the evolution of the log-likelihood during the estimation by importance sampling
saemix.plot.llis：图表显示的进化过程中的对数似然估计的重要性采样

saemix.plot.obsvspredlot 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)
saemix.plot.obsvspred的意见（左），和图的各个参数与观测计算与预测的人口参数与计算的预测：图（右）

saemix.plot.scatterresiduals:Scatterplot of the residuals versus the predictor (top) and versus predictions (bottom), for weighted residuals (population residuals, left), individual weighted residuals (middle) and npde (right).
saemix.plot.scatterresiduals：残差与预测（上）和与预测（下），加权残值法（人口残差左），个别加权残值法（中）和npde（右）的散点图。

saemix.plot.distribresidualsistribution of the residuals, plotted as histogram (top) and as a QQ-plot (bottom), for weighted residuals (population residuals, left), individual weighted residuals (middle) and npde (right).
saemix.plot.distribresiduals：残差分布，绘制直方图（上）和一个QQ图（下），加权残值法（人口残差左），个别加权残值法（中）和npde（右）。

saemix.plot.fits:Model fits. Individual fits are obtained using the individual parameters with the individual covariates. Population fits are obtained using the population parameters with the individual covariates (red) and the individual parameters with the individual covariates (green). By default the individual plots are displayed.
saemix.plot.fits：模型拟合。个人适合使用单独的参数与个人的协变量。人口适合使用的人口参数与个人的协变量（红色）和各个参数与个人的协变量（绿色）。默认情况下，个别图被显示出来。

saemix.plot.distpsiistribution of the parameters (conditional on covariates when some are included in the model). A histogram of individual parameter estimates can be overlayed on the plot, but it should be noted that the histogram does not make sense when there are covariates influencing the parameters and a warning will be displayed
saemix.plot.distpsi：分布参数（有条件时，一些包含在模型中的协变量）。个别参数估计的柱状图可以被覆盖的图，但应注意，直方图没有任何意义时，有协变量的影响参数，将显示警告

saemix.plot.randeff:Boxplot of the random effects
saemix.plot.randeff：盒形图的随机效应

saemix.plot.correlations:Correlation between the random effects
saemix.plot.correlations：随机效应之间的相关性

saemix.plot.parcovlots of the estimates of the individual parameters versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
saemix.plot.parcov图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

saemix.plot.randeffcovlots of the estimates of the random effects versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
saemix.plot.randeffcov：图与协变量的随机效应的估计，使用连续的变量散点图，盒形图分类协变量

saemix.plot.npdelots 4 graphs to evaluate the shape of the distribution of the normalised prediction distribution errors (npde)
saemix.plot.npde：图解4的归一化预测分布误差的分布的形状的图形以评估（npde）

saemix.plot.vpc:Visual Predictive Check, with options to include the prediction intervals around the boundaries of the selected interval as well as around the median (50th percentile of the simulated data). Several methods are available to define binning on the X-axis (see methods in the PDF guide).   
saemix.plot.vpc：视觉预测查看，选项，包括在选定的时间间隔的边界周围的预测间隔以及周围的中位数（模拟数据）的第50个百分。有几种方法可用来定义像素合并在X-轴（见在PDF导向的方法）。

Each plot can be customised by modifying options, either through a list of options set by the saemix.plot.setoptions function, or on the fly by passing an option in the call to the plot (see examples).
每个小区可以自定义修改选项，通过设置的选项列表saemix.plot.setoptions功能，或在飞行中通过调用的图中的一个选项（参见示例）。


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, saemix.plot.setoptions, saemix.plot.select, plot.saemix
SaemixObject，saemix，saemix.plot.setoptions，saemix.plot.select，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(seed=632545,save=FALSE,save.graphs=FALSE)

saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

# Simulate data and compute weighted residuals and npde[模拟数据和计算的加权残值法和npde]
saemix.fit<-compute.sres(saemix.fit)

# Data[数据]
saemix.plot.data(saemix.fit)

# Convergence[收敛]
saemix.plot.convergence(saemix.fit)

# Individual plot for subject 1, smoothed[个别图为主题，平滑]
saemix.plot.fits(saemix.fit,ilist=1,smooth=TRUE)

# Individual plot for subject 1 to 12, with ask set to TRUE [主题1到12个人的图，与要求设置为TRUE]
# (the system will pause before a new graph is produced)[（系统会暂停之前产生一个新的图形）]
saemix.plot.fits(saemix.fit,ilist=c(1:12),ask=TRUE)

# Diagnostic plot: observations versus population predictions[诊断的图：观察与人口预测]
par(mfrow=c(1,1))
saemix.plot.obsvspred(saemix.fit,level=0,new=FALSE)

# LL by Importance Sampling[LL重要性抽样]
saemix.plot.llis(saemix.fit)

# Scatter plot of residuals[对残差的散点图]
saemix.plot.scatterresiduals(saemix.fit)

# Boxplot of random effects[盒形图的随机效应]
saemix.plot.randeff(saemix.fit)

# Relationships between parameters and covariates[参数和协变量之间的关系]
saemix.plot.parcov(saemix.fit)

# Relationships between parameters and covariates, on the same page[参数和协变量之间的关系，在同一页上]
par(mfrow=c(3,2))
saemix.plot.parcov(saemix.fit,new=FALSE)

# VPC, default options (10 bins, equal number of observations in each bin)[VPC，默认选项（10箱，在每个容器相等的若干意见）]
saemix.plot.vpc(saemix.fit)

# VPC, user-defined breaks for binning[VPC，用户自定义的截断，分级]
saemix.plot.vpc(saemix.fit,vpc.method="user",
  vpc.breaks=c(0.4,0.8,1.5,2.5,4,5.5,8,10,13))

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


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