plot.survFitVarExp: Plotting method for `survFit` objects

Description

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

Usage

# S3 method for survFitVarExp
plot(
  x,
  xlab = "Time",
  ylab = "Survival probability",
  main = NULL,
  spaghetti = FALSE,
  one.plot = FALSE,
  adddata = TRUE,
  mcmc_size = NULL,
  scales = "fixed",
  addConfInt = TRUE,
  ...
)

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.
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.
mcmc_size: A numerical value refering by default to the size of the mcmc in object survFit. This option is specific to survFitVarExp objects for which computing time may be long. mcmc_size can be used to reduce the number of mcmc samples in order to speed up the computation.
scales: Shape the scale of axis. Default is "fixed", but can be "free", or free in only one dimension "free_x", "free_y". (See ggplot2 documentation for more details.)
addConfInt: If TRUE, add a \(95\%\) confidence interval on observed data from a binomial test
...: 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 profile. 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% binomial 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 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 (10% of the MCMC chains are randomly taken for this sample).