plotModels function

Plot candidate models

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.

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

# 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)