MCPMod (version 1.0-10.1)

plot.powerMM: Plot method for powerMM objects

Description

This function plots the result of the powerMM function call in a trellis display.

Usage

# S3 method for powerMM
plot(x, superpose = TRUE, line.at = NULL, models = "all",
     summ = NULL, perc = FALSE, xlab = NULL,
     ylab = ifelse(perc, "Power (%)", "Power"), ...)

Arguments

x

A powerMM object, i.e. a matrix with power values for different sample sizes and models

superpose

Logical, indicating if lines should be superposed.

line.at

A value, or a vector of values, between 0 and 1, to be drawn as horizontal line in the plot (default: not drawn).

models

Character determining which of the models should be included in the plot, "all" and "none" are accepted, else names (or numbers) of models.

summ

Summaries to be included in plot; by default the mean, the minimum and the maximum value are displayed.

perc

Logical indicating if power values should be in percentage.

xlab

Label for x-axis.

ylab

Label for y-axis.

…

Additional arguments for the xyplot function.

References

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

powerMM

Examples

# NOT RUN {
# Example from JBS paper
doses <- c(0,10,25,50,100,150)                                         
models <- list(linear = NULL, emax = 25,                               
               logistic = c(50, 10.88111), exponential= 85,            
               betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2))
pM <- powerMM(models, doses, base = 0, maxEff = 0.4, sigma = 1,       
               lower = 10, upper = 100, step = 20, scal = 200)        
pM
plot(pM)
plot(pM, line.at = 0.8, model = c("emax", "linear"), summ = "mean")
plot(pM, line.at = 0.8, model = "none", summ = c("median", "min"))
# }