plot.estimate_R | R Documentation |
The plot method of estimate_r
objects can be used to visualise three
types of information. The first one shows the epidemic curve. The second one
shows the posterior mean and 95% credible interval of the reproduction
number. The estimate for a time window is plotted at the end of the time
window. The third plot shows the discrete distribution(s) of the serial
interval.
## S3 method for class 'estimate_R'
plot(
x,
what = c("all", "incid", "R", "SI"),
plot_theme = "v2",
add_imported_cases = FALSE,
options_I = list(col = palette(), transp = 0.7, xlim = NULL, ylim = NULL, interval =
1L, xlab = "Time", ylab = "Incidence"),
options_R = list(col = palette(), transp = 0.2, xlim = NULL, ylim = NULL, xlab =
"Time", ylab = "R"),
options_SI = list(prob_min = 0.001, col = "black", transp = 0.25, xlim = NULL, ylim =
NULL, xlab = "Time", ylab = "Frequency"),
legend = TRUE,
...
)
x |
The output of function |
what |
A string specifying what to plot, namely the incidence time
series ( |
plot_theme |
A string specifying whether to use the original plot theme (plot_theme = "original") or an alternative plot theme (plot_theme = "v2"). The plot_theme is "v2" by default. |
add_imported_cases |
A boolean to specify whether, on the incidence time series plot, to add the incidence of imported cases. |
options_I |
For what = "incid" or "all". A list of graphical options:
|
options_R |
For what = "R" or "all". A list of graphical options:
|
options_SI |
For what = "SI" or "all". A list of graphical options:
|
legend |
A boolean (TRUE by default) governing the presence / absence of legends on the plots |
... |
further arguments passed to other methods (currently unused). |
a plot (if what = "incid"
, "R"
, or "SI"
) or a
grob
object (if what = "all"
).
Rolina van Gaalen rolina.van.gaalen@rivm.nl and Anne Cori a.cori@imperial.ac.uk; S3 method by Thibaut Jombart; v2 theme by Rebecca Nash
estimate_R
,
wallinga_teunis
and
estimate_R_plots
## load data on pandemic flu in a school in 2009
data("Flu2009")
## estimate the instantaneous reproduction number
## (method "non_parametric_si")
R_i <- estimate_R(Flu2009$incidence,
method = "non_parametric_si",
config = list(
t_start = seq(2, 26),
t_end = seq(8, 32),
si_distr = Flu2009$si_distr
)
)
## visualise results
plot(R_i, legend = FALSE)
## estimate the instantaneous reproduction number
## (method "non_parametric_si")
R_c <- wallinga_teunis(Flu2009$incidence,
method = "non_parametric_si",
config = list(
t_start = seq(2, 26),
t_end = seq(8, 32),
si_distr = Flu2009$si_distr,
n_sim = 10
)
)
## produce plot of the incidence
## (with, on top of total incidence, the incidence of imported cases),
## estimated instantaneous and case reproduction numbers
## and serial interval distribution used
p_I <- plot(R_i, "incid", add_imported_cases=TRUE) # plots the incidence
p_SI <- plot(R_i, "SI") # plots the serial interval distribution
p_Ri <- plot(R_i, "R",
options_R = list(ylim = c(0, 4)))
# plots the estimated instantaneous reproduction number
p_Rc <- plot(R_c, "R",
options_R = list(ylim = c(0, 4)))
# plots the estimated case reproduction number
gridExtra::grid.arrange(p_I, p_SI, p_Ri, p_Rc, ncol = 2)
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