This function plots the result of the powerMM function call in a trellis display.
## S3 method for class '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 paperdoses <- 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"))## End(Not run)