plot.survFitCstExp: Plotting method for `survFit` objects

Description

This is the generic plot S3 method for the survFit. It plots the fit obtained for each concentration of chemical compound in the original dataset.

Usage

# S3 method for survFitCstExp
plot(
  x,
  xlab = "Time",
  ylab = "Survival probability",
  main = NULL,
  concentration = NULL,
  spaghetti = FALSE,
  one.plot = FALSE,
  adddata = TRUE,
  addlegend = FALSE,
  style = "ggplot",
  ...
)

Value

a plot of class ggplot

Arguments

x: An object of class survFit.
xlab: A label for the \(X\)-axis, by default Time.
ylab: A label for the \(Y\)-axis, by default Survival probability.
main: A main title for the plot.
concentration: A numeric value corresponding to some specific concentrations in data. If concentration = NULL, draws a plot for each concentration.
spaghetti: if TRUE, draws a set of survival curves using parameters drawn from the posterior distribution
one.plot: if TRUE, draws all the estimated curves in one plot instead of one plot per concentration.
adddata: if TRUE, adds the observed data to the plot with (frequentist binomial) confidence intervals
addlegend: if TRUE, adds a default legend to the plot.
style: graphical backend, can be 'generic' or 'ggplot'
...: Further arguments to be passed to generic methods.

Details

The fitted curves represent the estimated survival probability as a function of time for each concentration. The black dots depict the observed survival probability at each time point. 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 error bars 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 types of 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 (2% of the MCMC chains are randomly taken for this sample).