saemix.plot.select | R Documentation |

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 |

`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 (we suggest to use |

`npde` |
if TRUE, produce plots of the npde. Defaults to FALSE (deprecated in 3.0, please use |

`...` |
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 (deprecated in 3.0, please use

`npdeSaemix`

instead)- vpc
Visual Predictive Check (we suggest to use

`npdeSaemix`

instead)

None

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

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

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

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

, `npdeSaemix`

`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)
```

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