plotDiscreteData {saemix}

Plot non Gaussian data

Description

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

Usage

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 #'

Author(s)

Emmanuelle Comets [e-mail]

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

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")