morse: plot.survFitTT – R documentation

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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 class '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",
  ...
)

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).

Note

When style = "ggplot", the function calls function ggplot and returns an object of class ggplot.

Examples

# (1) Load the data
data(cadmium1)

# (2) Create an object of class "survData"
dat <- survData(cadmium1)

## Not run: 
# (3) Run the survFitTT function with the log-logistic
#     binomial model
out <- survFitTT(dat, lcx = c(5, 10, 15, 20, 30, 50, 80),
                 quiet = TRUE)

# (4) Plot the fitted curve
plot(out, log.scale = TRUE, adddata = TRUE)

# (5) Plot the fitted curve with ggplot style
plot(out, xlab = expression("Concentration in" ~ mu~g.L^{-1}),
     fitcol = "blue", adddata = TRUE, cicol = "blue",
     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