plotModels {MCPMod} | R Documentation |
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", ...)
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. |
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
# 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)