plot.survFitTT: Plotting method for `survFitTT` objects

Description

This is the generic plot S3 method for the survFitTT class. It plots concentration-response fit under target time survival analysis.

Usage

# S3 method for survFitTT
plot(
  x,
  xlab = "Concentration",
  ylab = "Survival probability",
  main = NULL,
  fitcol = "orange",
  fitlty = 1,
  fitlwd = 1,
  spaghetti = FALSE,
  cicol = "orange",
  cilty = 2,
  cilwd = 1,
  ribcol = "grey70",
  adddata = FALSE,
  addlegend = FALSE,
  log.scale = FALSE,
  style = "ggplot",
  ...
)

Value

a plot of class ggplot

Arguments

x: an object of class survFitTT
xlab: a label for the \(X\)-axis, default is Concentration
ylab: a label for the \(Y\)-axis, default is Survival probability
main: main title for the plot
fitcol: color of the fitted curve
fitlty: line type of the fitted curve
fitlwd: width of the fitted curve
spaghetti: if TRUE, the credible interval is represented by multiple curves
cicol: color of the 95 % credible interval limits
cilty: line type for the 95 % credible interval limits
cilwd: width of the 95 % credible interval limits
ribcol: color of the ribbon between lower and upper credible limits. Transparent if NULL
adddata: if TRUE, adds the observed data with confidence intervals to the plot
addlegend: if TRUE, adds a default legend to the plot
log.scale: if TRUE, displays \(X\)-axis in log-scale
style: graphical backend, can be 'generic' or 'ggplot'
...: Further arguments to be passed to generic methods

Details

The fitted curve represents the estimated survival probability at the target time as a function of the concentration of chemical compound; When adddata = TRUE the black dots depict the observed survival probability at each tested concentration. Note that since our model does not take inter-replicate variability into consideration, replicates are systematically pooled in this plot. The function plots both 95% credible intervals for the estimated survival probability (by default the grey area around the fitted curve) and 95% binomial confidence intervals for the observed survival probability (as black segments if adddata = TRUE). Both types of intervals are taken at the same level. Typically a good fit is expected to display a large overlap between the two intervals. If spaghetti = TRUE, the credible intervals are represented by two dotted lines limiting the credible band, and a spaghetti plot is added to this band. This spaghetti plot consists of the representation of simulated curves using parameter values sampled in the posterior distribution (10% of the MCMC chains are randomly taken for this sample).