saemix.plot.select: Plots of the results obtained by SAEM
In saemixdevelopment/saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Description Usage Arguments Details Value Author(s) References See Also Examples

Several plots (selectable by the type argument) are currently available: convergence plot, individual plots, predictions versus observations, distribution plots, residual plots, VPC.

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,
  ...
)

`saemixObject`	an object returned by the `saemix` function
`data`	if TRUE, produce a plot of the data. Defaults to FALSE
`convergence`	if TRUE, produce a convergence plot. Defaults to FALSE
`likelihood`	if TRUE, produce a plot of the estimation of the LL by importance sampling. Defaults to FALSE
`individual.fit`	if TRUE, produce individual fits with individual estimates. Defaults to FALSE
`population.fit`	if TRUE, produce individual fits with population estimates. Defaults to FALSE
`both.fit`	if TRUE, produce individual fits with both individual and population estimates. Defaults to FALSE
`observations.vs.predictions`	if TRUE, produce a plot of observations versus predictions. Defaults to FALSE
`residuals.scatter`	if TRUE, produce scatterplots of residuals versus predictor and predictions. Defaults to FALSE
`residuals.distribution`	if TRUE, produce plots of the distribution of residuals. Defaults to FALSE
`random.effects`	if TRUE, produce boxplots of the random effects. Defaults to FALSE
`correlations`	if TRUE, produce a matrix plot showing the correlation between random effects. Defaults to FALSE
`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
`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
`marginal.distribution`	if TRUE, produce plots of the marginal distribution of the random effects. Defaults to FALSE
`vpc`	if TRUE, produce Visual Predictive Check plots. Defaults to FALSE
`npde`	if TRUE, produce plots of the npde. Defaults to FALSE
`...`	optional arguments passed to the plots

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.

data: A spaghetti plot of the data, displaying the observed data y as a function of the regression variable (eg time for a PK application)
convergence: For each parameter in the model, this plot shows the evolution of the parameter estimate versus the iteration number
likelihood: Estimation of the likelihood estimated by importance sampling, as a function of the number of MCMC samples
individual.fit: Individual fits, using the individual parameters with the individual covariates
population.fit: Individual fits, using the population parameters with the individual covariates
both.fit: Individual fits, using the population parameters with the individual covariates and the individual parameters with the individual covariates
observations.vs.predictions: Plot 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)
residuals.scatter: Scatterplot 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.distribution: Distribution 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
random.effects: Boxplot of the random effects
correlations: Correlation between the random effects, with a smoothing spline
parameters.versus.covariates: Plots of the estimate of the individual parameters versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
randeff.versus.covariates: Plots of the estimate of the individual random effects versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
marginal.distribution: Distribution of each parameter in the model (conditional on covariates when some are included in the model)
npde: Plot of npde as in package npde
vpc: Visual Predictive Check

None

Emmanuelle Comets <emmanuelle.comets@inserm.fr>, Audrey Lavenu, Marc Lavielle.

Comets E, Lavenu A, Lavielle M. Parameter estimation in nonlinear mixed effect models using saemix, an R implementation of the SAEM algorithm. Journal of Statistical Software 80, 3 (2017), 1-41.

Kuhn E, Lavielle M. Maximum likelihood estimation in nonlinear mixed effects models. Computational Statistics and Data Analysis 49, 4 (2005), 1020-1038.

Comets E, Lavenu A, Lavielle M. SAEMIX, an R version of the SAEM algorithm. 20th meeting of the Population Approach Group in Europe, Athens, Greece (2011), Abstr 2173.

SaemixObject,saemix, default.saemix.plots, saemix.plot.setoptions, saemix.plot.data, saemix.plot.convergence, saemix.plot.llis, saemix.plot.randeff, saemix.plot.obsvspred, saemix.plot.fits, saemix.plot.parcov, saemix.plot.randeffcov, saemix.plot.distpsi, saemix.plot.scatterresiduals, saemix.plot.distribresiduals, saemix.plot.vpc

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)

# Not run (strict time constraints for CRAN)
# 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
# par(mfrow=c(2,2))
# saemix.plot.select(saemix.fit,data=TRUE,vpc=TRUE,observations.vs.predictions=TRUE, new=FALSE)