plot.mcmc.dsm.tvp {shrinkDSM} | R Documentation |
Graphical summary of posterior distribution for a piecewise constant, time-varying parameter
Description
plot.mcmc.dsm.tvp
plots empirical posterior quantiles for a piecewise constant, time-varying parameter.
Usage
## S3 method for class 'mcmc.dsm.tvp'
plot(
x,
probs = c(0.025, 0.25, 0.75, 0.975),
shaded = TRUE,
quantlines = FALSE,
shadecol = "skyblue",
shadealpha = 0.5,
quantlty = 2,
quantcol = "black",
quantlwd = 0.5,
drawzero = TRUE,
zerolty = 2,
zerolwd = 1,
zerocol = "grey",
...
)
Arguments
x |
|
probs |
vector of boundaries for credible intervals to plot for each point in time, with values in [0,1].
The largest and smallest value form the outermost credible interval, the second smallest and second largest the second outermost and so forth.
The default value is |
shaded |
single logical value or a vector of logical values, indicating whether or not to shade the area between the pointwise
credible intervals. If a vector is given, the first value given is used to determine if the area between the outermost credible interval
is shaded, the second for the second outermost and so forth. Recycled in the usual fashion if the vector is shorter than the
number of quantile pairs. The default value is |
quantlines |
single logical value or a vector of logical values, indicating whether or not to draw borders along the pointwise
credible intervals. If a vector is given, the first value given is used to determine whether the outermost credible interval
is marked by lines, the second for the second outermost and so forth. Recycled in the usual fashion if the vector is shorter than the
number of credible intervals. The default value is |
shadecol |
single character string or a vector of character strings. Determines the color of the shaded areas that represent
the credible intervals. If a vector is given, the first color given is used for the outermost area,
the second for the second outermost and so forth. Recycled in the usual fashion if the vector is shorter than the number
of shaded areas. Has no effect if |
shadealpha |
real number between 0 and 1 or a vector of real numbers between 0 and 1.
Determines the level of transparency of the shaded areas that represent
the credible intervals. If a vector is given, the first value
given is used for the outermost area, the second for the second outermost and so forth. Recycled in the usual fashion
if the vector is shorter than the number of shaded areas. Has no effect if |
quantlty |
either a single integer in [0,6] or one of the character strings |
quantcol |
single character string or a vector of character strings. Determines the color of the borders drawn around the shaded
areas that represent the credible intervals. If a vector is given, the first color given is used for borders of the outermost area,
the second for the second outermost and so forth. Recycled in the usual fashion if the vector is shorter than the number
of shaded areas. Has no effect if |
quantlwd |
single real, positive number or a vector of real, positive numbers. Determines the line width of the borders
drawn around the shaded areas that represent the credible intervals. If a vector is given, the first number given is used for
the borders of the outermost area, the second for the second outermost and so forth. Recycled in the usual fashion if the vector
is shorter than the number of shaded areas. Has no effect if |
drawzero |
single logical value determining whether to draw a horizontal line at zero or not. The default value is |
zerolty |
single integer in [0,6] or one of the character strings |
zerolwd |
single real, positive number. Determines the line width of the horizontal line at zero. Has no effect
if |
zerocol |
single character string. Determines the color of the horizontal line at zero. Has no effect if |
... |
further arguments to be passed to |
Value
Called for its side effects and returns invisibly.
Author(s)
Peter Knaus peter.knaus@wu.ac.at
See Also
Other plotting functions:
plot.shrinkDSM_pred()
,
plot.shrinkDSM()
Examples
set.seed(123)
data("gastric")
# Create intervals for piecewise exponential model
intervals <- divisionpoints(gastric$time, gastric$status, 2)
# Estimate model
mod <- shrinkDSM(time ~ radiation, gastric,
delta = gastric$status, S = intervals)
# Plot piecewise constant, time-varying parameter
plot(mod$beta$beta_radiation)