morse: plot.survFitTKTD – R documentation

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plot.survFitTKTD

Plotting method for survFitTKTD objects

Description

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

Usage

## S3 method for class 'survFitTKTD'
plot(
  x,
  xlab = "Time",
  ylab = "Survival probablity",
  main = NULL,
  concentration = NULL,
  spaghetti = FALSE,
  one.plot = FALSE,
  adddata = FALSE,
  addlegend = FALSE,
  style = "ggplot",
  ...
)

Arguments

`x`	An object of class `survFitTKTD`.
`xlab`	A label for the X-axis, by default `Time`.
`ylab`	A label for the Y-axis, by default `Survival probablity`.
`main`	A main title for the plot.
`concentration`	A numeric value corresponding to some specific concentration 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 probablity as a function of time for each concentration When adddata = TRUE the black dots depict the observed survival probablity 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 probablity (by default the grey area around the fitted curve) and 95% binomial confidence intervals for the observed survival probablity (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).

Examples

# (1) Load the survival data
data(propiconazole)

# (2) Create an object of class "survData"
dataset <- survData(propiconazole)

## Not run: 
# (3) Run the survFitTKTD function ('SD' model only)
out <- survFitTKTD(dataset)

# (4) Plot the fitted curves in one plot 
plot(out)

# (5) Plot one fitted curve per concentration with credible limits as
# spaghetti, data and confidence intervals
# and with a ggplot style
plot(out, spaghetti = TRUE , adddata = TRUE, one.plot = FALSE,
     style = "ggplot")

# (6) Plot fitted curve for one specific concentration
plot(out, concentration = 36, style = "ggplot")

## End(Not run)

morse

Modelling Tools for Reproduction and Survival Data in Ecotoxicology

v3.3.1

GPL (>= 2)

Authors

Virgile Baudrot [aut], Sandrine Charles [aut], Marie Laure Delignette-Muller [aut], Wandrille Duchemin [ctb], Benoit Goussen [ctb], Nils Kehrein [ctb], Guillaume Kon-Kam-King [ctb], Christelle Lopes [ctb], Philippe Ruiz [aut], Alexander Singer [ctb], Philippe Veber [aut]

Initial release