plotDiscreteData function

Plot non Gaussian data

This function provides exploration plots for non Gaussian longitudinal data (work in progress, doesn't work yet for RTTE)

plotDiscreteData(object, outcome = "continuous", verbose = FALSE, ...)

plotDiscreteDataElement(
  object,
  outcome = "categorical",
  mirror = FALSE,
  irep = 1,
  verbose = FALSE,
  ...
)

Arguments

• object: an SaemixData object returned by the saemixData function. For plotDiscreteDataElement, an SaemixObject object returned by the saemix 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)
• mirror: if TRUE, plots a mirror plot of the same type as the data (the object must include simulated data)
• irep: number of the replication to use in the mirror plot

Details

This function is a very rough first attempt at automatically creating plots to explore discrete longitudinal data.

• for TTE data, a KM plot will be produced
• for count, categorical and binary data, a plot showing the proportion of each score/category across time will be shown 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

# Time-to-event data
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")))

# Plots a KM survival plot
plotDiscreteData(saemix.data, outcome="TTE")
# Plots a KM survival plot, stratified by sex
plotDiscreteData(saemix.data, outcome="TTE", which.cov="sex")

# Count data
data(rapi.saemix)
saemix.data<-saemixData(name.data=rapi.saemix, name.group=c("id"),
                 name.predictors=c("time","rapi"),name.response=c("rapi"),
                 name.covariates=c("gender"),units=list(x="months",y="",covariates=c("")))

# Plots a histogram of the counts
plotDiscreteData(saemix.data, outcome="count")

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