View source: R/plot.survFitVarExp.R
plot.survFitVarExp | R Documentation |
survFit
objectsThis is the generic plot
S3 method for the
survFit
. It plots the fit obtained for each
concentration profile in the original dataset.
## S3 method for class 'survFitVarExp'
plot(
x,
xlab = "Time",
ylab = "Survival probability",
main = NULL,
spaghetti = FALSE,
one.plot = FALSE,
adddata = TRUE,
mcmc_size = NULL,
scales = "fixed",
addConfInt = TRUE,
...
)
x |
An object of class |
xlab |
A label for the |
ylab |
A label for the |
main |
A main title for the plot. |
spaghetti |
if |
one.plot |
if |
adddata |
if |
mcmc_size |
A numerical value refering by default to the size of the mcmc in object |
scales |
Shape the scale of axis. Default is |
addConfInt |
If |
... |
Further arguments to be passed to generic methods. |
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).
a plot of class ggplot
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