View source: R/estimate_R_plots.R
estimate_R_plots | R Documentation |
This wrapper has been created so that several estimate_R
objects can
be plotted at the same time.
estimate_R_plots(..., legend = FALSE)
... |
Arguments of
|
legend |
A boolean (TRUE by default) governing the presence / absence of legends on the plots |
a plot (if what = "incid"
, "R"
, or "SI"
) or a
grob
object (if what = "all"
).
Anne Cori, Zhian Kamvar
plot.estimate_R
## load data on pandemic flu in a school in 2009
data("Flu2009")
#### COMPARE THE INSTANTANEOUS AND CASE REPRODUCTION NUMBERS ####
## estimate the instantaneous reproduction number
## (method "non_parametric_si")
R_instantaneous <- 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
)
)
## estimate the case reproduction number
R_case <- 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
)
)
## visualise R estimates on the same plot
estimate_R_plots(list(R_instantaneous, R_case), what = "R",
options_R = list(col = c("blue", "red")), legend = TRUE)
#### COMPARE THE INSTANTANEOUS R ON SLIDING WEEKLY OR BIWEEKLY WINDOWS ####
R_weekly <- estimate_R(Flu2009$incidence,
method = "non_parametric_si",
config = list(t_start = seq(9, 26),
t_end = seq(15, 32),
si_distr = Flu2009$si_distr
)
)
R_biweekly <- estimate_R(Flu2009$incidence,
method = "non_parametric_si",
config = list(t_start = seq(2, 19),
t_end = seq(15, 32),
si_distr = Flu2009$si_distr
)
)
## visualise R estimates on the same plot
estimate_R_plots(list(R_weekly, R_biweekly), what = "R",
options_R = list(col = c("blue", "red")), legend = TRUE)
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