plot.survFitPredict: Plotting method for 'survFitPredict' objects

In morse: Modelling Reproduction and Survival Data in Ecotoxicology

Plotting method for survFitPredict objects

Description

This is the generic plot S3 method for the survFitPredict. It plots the predicted survival probability for each concentration of the chemical compound in the provided dataset.

Usage

## S3 method for class 'survFitPredict'
plot(
  x,
  xlab = "Time",
  ylab = "Survival probability",
  main = NULL,
  spaghetti = FALSE,
  one.plot = FALSE,
  mcmc_size = NULL,
  ...
)

Arguments

x	An object of class survFitPredict.
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.
mcmc_size	A numerical value refering by default to the size of the mcmc in object survFitPredict. This option is specific to survFitPredict 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.
...	Further arguments to be passed to generic methods.

Details

The fitted curves represent the predicted survival probability as a function of time for each concentration. The function plots both the 95% credible band and the predicted survival probability over time. 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).

Value

a plot of class ggplot

Examples

# (1) Load the survival data
data("propiconazole_pulse_exposure")

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


# (3) Run the survFit function
out <- survFit(dataset , model_type = "SD")

# (4) Create a new data table for prediction
data_4prediction <- data.frame(time = 1:10, conc = c(0,5,5,5,0,0,5,5,5,5),
 replicate= rep("predict", 10))

# (5) Predict on a new dataset
predict_out <- predict(out, data_predict = data_4prediction, spaghetti = TRUE)

# (6) Plot the predicted curve
plot(predict_out)
plot(predict_out, spaghetti = TRUE)

morse documentation built on Oct. 29, 2022, 1:14 a.m.