plotDiscreteData: Plot non Gaussian data

In saemix: Stochastic Approximation Expectation Maximization (SAEM) Algorithm

Plot non Gaussian data

Description

This function provides exploration plots for non Gaussian longitudinal data (work in progress)

Usage

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

Arguments

object	an SaemixData object returned by the saemixData 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 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")

saemix documentation built on July 9, 2023, 7:43 p.m.