plot.epiman {amei} | R Documentation |
These functions provide a visualization of the evolution of an epidemic, or multiple epidemics obtained via Monte Carlo, and the associated costs of the vaccination strategy employed
## S3 method for class 'epiman': plot(x, type = c("epi", "costs", "params", "fracs", "stops"), showd = FALSE, main = NULL, true = NULL, ...) ## S3 method for class 'MCepi': plot(x, type = c("epi", "costs", "fracs", "stops"), showd = FALSE, showv = FALSE, main = NULL, ...)
x |
the object to be plotted, either of class
"MCepi" or "epiman"
|
type |
indicates the type of plot to be produced, including
the epidemic trajectory/ies ("epi" , the default), cost(s),
estimated distribution(s) of parameters ("params" , only
in the case of "epiman" -class objects), the fraction
vaccinated at each time step ("fracs" ), and the
vaccination (stopping) threshold ("stops" ) |
main |
optional title argument for the plot. If not specified,
then an automatically generated default is used depending on the
type of plot specified |
true |
this argument only applies to plot.epiman with
type = "params" where it should be a list with scalar
entries $b , $k , $nu , and $mu indicating
the true parameter entries for the evolution of the epidemic to be
added (for comparison) to the posterior density plots |
showd |
logical indicating if deaths should be
shown in the trajectory plots when type = "epi" |
showv |
this argument only applies to plot.MCepi with
type = "epi" where it should be a logical indicating if
vaccinations should be shown in the trajectory plot
|
... |
additional arguments passed to plot |
The functions documented here support visualization
of "MCepi"
-class objects which are generated by the
MCepi
and MCmanage
function,
and "epiman"
-class objects are
generated by the manage
function. In both cases they
enable a visualization of the evolution of the resulting epidemic(s)
and costs associated with deaths, vaccinations, etc.
The only output of this function is beautiful plots
Daniel Merl <dan@stat.duke.edu>, Leah R. Johnson <leah@statslab.cam.ac.uk>, Robert B. Gramacy <bobby@statslab.cam.ac.uk>, and Mark S. Mangel <msmangl@ams.ucsc.edu>
A statistical framework for the adaptive management of epidemiological interventions (2008). Daniel Merl, Leah R. Johnson, Robert B. Gramacy, and Marc S. Mangel. Duke Working Paper 08-29. http://ftp.stat.duke.edu/WorkingPapers/08-29.html
## for examples of the usage of these functions, ## please see the documentation for the functions ## listed in the See Also section, above