plot.survFitTKTD {morse} | R Documentation |
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 |
xlab |
A label for the |
ylab |
A label for the |
main |
A main title for the plot. |
concentration |
A numeric value corresponding to some specific concentration in
|
spaghetti |
if |
one.plot |
if |
adddata |
if |
addlegend |
if |
style |
graphical backend, can be |
... |
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).
Value
a plot of class ggplot
Examples
# (1) Load the survival data
data(propiconazole)
# (2) Create an object of class "survData"
dataset <- survData(propiconazole)
# (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")