# VPC for non Gaussian data models This function provides VPC plots for non Gaussian data models (work in progress) ```r discreteVPC(object, outcome = "categorical", verbose = FALSE, ...) ``` ## Arguments - `object`: an saemixObject object returned by the `saemix` function. The object must include simulated data under the empirical design, using the model and estimated parameters from a fit, produced via the `simulateDiscreteSaemix` function. - `outcome`: type of outcome (valid types are "TTE", "binary", "categorical", "count") - `verbose`: whether to print messages (defaults to FALSE) - ``...``: additional arguments, used to pass graphical options (to be implemented, currently not available) ## Details This function is a very rough first attempt at automatically creating VPC plots for models defined through their log-likelihood (categorical, count, or TTE data). It makes use of the new element simulate.function in the model component of the object * for TTE data, a KM-VPC plot will be produced * for count, categorical and binary data, a plot showing the proportion of each score/category across time will be shown along with the corresponding prediction intervals from the model These plots can be stratified over a covariate in the data set (currently only categorical covariates) by passing an argument which.cov='name' to the call ## Examples ```r data(lung.saemix) saemix.data<-saemixData(name.data=lung.saemix,header=TRUE,name.group=c("id"), name.predictors=c("time","status","cens"),name.response=c("status"), name.covariates=c("age", "sex", "ph.ecog", "ph.karno", "pat.karno", "wt.loss","meal.cal"), units=list(x="days",y="",covariates=c("yr","","-","%","%","cal","pounds"))) weibulltte.model<-function(psi,id,xidep) { T<-xidep[,1] y<-xidep[,2] # events (1=event, 0=no event) cens<-which(xidep[,3]==1) # censoring times (subject specific) init <- which(T==0) lambda <- psi[id,1] # Parameters of the Weibull model beta <- psi[id,2] Nj <- length(T) ind <- setdiff(1:Nj, append(init,cens)) # indices of events hazard <- (beta/lambda)*(T/lambda)^(beta-1) # H' H <- (T/lambda)^beta # H logpdf <- rep(0,Nj) # ln(l(T=0))=0 logpdf[cens] <- -H[cens] + H[cens-1] # ln(l(T=censoring time)) logpdf[ind] <- -H[ind] + H[ind-1] + log(hazard[ind]) # ln(l(T=event time)) return(logpdf) } simulateWeibullTTE <- function(psi,id,xidep) { T<-xidep[,1] y<-xidep[,2] # events (1=event, 0=no event) cens<-which(xidep[,3]==1) # censoring times (subject specific) init <- which(T==0) lambda <- psi[,1] # Parameters of the Weibull model beta <- psi[,2] tevent<-T Vj<-runif(dim(psi)[1]) tsim<-lambda*(-log(Vj))^(1/beta) # nsuj events tevent[T>0]<-tsim tevent[tevent[cens]>T[cens]] <- T[tevent[cens]>T[cens]] return(tevent) } saemix.model<-saemixModel(model=weibulltte.model,description="time model",modeltype="likelihood", simulate.function = simulateWeibullTTE, psi0=matrix(c(1,2),ncol=2,byrow=TRUE,dimnames=list(NULL, c("lambda","beta"))), transform.par=c(1,1),covariance.model=matrix(c(1,0,0,0),ncol=2, byrow=TRUE)) saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE, displayProgress=FALSE) tte.fit<-saemix(saemix.model,saemix.data,saemix.options) simtte.fit <- simulateDiscreteSaemix(tte.fit, nsim=100) gpl <- discreteVPC(simtte.fit, outcome="TTE") plot(gpl) ``` ## References Brendel, K, Comets, E, Laffont, C, Laveille, C, Mentre, F. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide, Pharmaceutical Research 23 (2006), 2036-2049. Holford, N. The Visual Predictive Check: superiority to standard diagnostic (Rorschach) plots (Abstract 738), in: 14th Meeting of the Population Approach Group in Europe, Pamplona, Spain, 2005. Ron Keizer, tutorials on VPC TODO ## See Also `SaemixObject`, `saemix`, `saemix.plot.vpc`, `simulateDiscreteSaemix` ## Author(s) Emmanuelle Comets [emmanuelle.comets@inserm.fr](mailto:emmanuelle.comets@inserm.fr)

discreteVPC function