EpiEstim (version 2.2-4)

estimate_R_plots: Wrapper for plot.estimate_R

Description

This wrapper has been created so that several estimate_R objects can be plotted at the same time.

Usage

estimate_R_plots(..., legend = FALSE)

Arguments

...

Arguments of plot.estimate_R, but in addition, parameter x can be a objects of class estimate_R (obtained as outputs of functions estimate_R or wallinga_teunis. If x is a list, and what='R' or what='all', all estimates of R are plotted on a single graph. This will only work if all the estimate_R objects in the list were computed using the same config$t_start and config$t_end

legend

A boolean (TRUE by default) governing the presence / absence of legends on the plots

Value

a plot (if what = "incid", "R", or "SI") or a grob object (if what = "all").

See Also

plot.estimate_R

Examples

# NOT RUN {
## 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)
# }