MCPMod (version 1.0-10.1)

plotModels: Plot candidate models

Description

Produces a trellis display of the model functions in the candidate set. The location and scale parameters of the models are determined by the base and maxEff arguments.

Usage

plotModels(models, doses, base, maxEff, nPoints = 200, 
           off = 0.1 * max(doses), scal = 1.2 * max(doses), 
           superpose = FALSE, ylab = "Model means", 
           xlab = "Dose", ...)

Arguments

models

A list specifying the candidate models. This can also be a fullMod object, then the arguments base, maxEff, off and scal are ignored.

doses

Dose levels to be administered

base

Expected baseline effect

maxEff

Expected maximum change from baseline

nPoints

Number of points for plotting

off

Offset parameter for the linear in log model (default: 10 percent of maximum dose)

scal

Scale parameter for the beta model (default: 20 percent larger than maximum dose)

superpose

Logical determining, whether model plots should be superposed

ylab, xlab

Label for y-axis and x-axis.

…

Additional arguments to the xyplot call.

References

Bornkamp B., Pinheiro J. C., Bretz, F. (2009). MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies, Journal of Statistical Software, 29(7), 1--23

Pinheiro, J. C., Bornkamp, B. and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statistics, 16, 639--656

See Also

guesst, fullMod

Examples

# NOT RUN {
# JBS example
doses <- c(0,10,25,50,100,150)
models <- list(linear = NULL, emax = c(25),
            logistic = c(50, 10.88111), exponential = c(85),
            betaMod = matrix(c(0.33, 2.31, 1.39, 1.39),
             byrow=TRUE, nrow=2))
plotModels(models, doses, base = 0, maxEff = 0.4, scal = 200) 
# all models in one panel
plotModels(models, doses, base = 0, maxEff = 0.4, scal = 200,
           superpose = TRUE)

# plotModels can also be called using a fullMod object
fM <- fullMod(models, doses, base = 0, maxEff = 0.4, scal = 200)
plotModels(fM)
# or even easier
plot(fM)
# }