plot.powerMM {MCPMod} | R Documentation |
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"), ...)
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. |
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
# 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"))