plot.estimate_R: Plot outputs of estimate_r

In EpiEstim: Estimate Time Varying Reproduction Numbers from Epidemic Curves

Description

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.

Usage

## S3 method for class 'estimate_R'
plot(
  x,
  what = c("all", "incid", "R", "SI"),
  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,
  ...
)

Arguments

x	The output of function estimate_R or function wallinga_teunis. To plot simultaneous outputs on the same plot use estimate_R_plots function
what	A string specifying what to plot, namely the incidence time series (what='incid'), the estimated reproduction number (what='R'), the serial interval distribution (what='SI', or all three (what='all')).
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: col A color or vector of colors used for plotting incid. By default uses the default R colors. transp A numeric value between 0 and 1 used to monitor transparency of the bars plotted. Defaults to 0.7. xlim A parameter similar to that in par, to monitor the limits of the horizontal axis ylim A parameter similar to that in par, to monitor the limits of the vertical axis interval An integer or character indicating the (fixed) size of the time interval used for plotting the incidence; defaults to 1 day. xlab, ylab Labels for the axes of the incidence plot
options_R	For what = "R" or "all". A list of graphical options: col A color or vector of colors used for plotting R. By default uses the default R colors. transp A numeric value between 0 and 1 used to monitor transparency of the 95%CrI. Defaults to 0.2. xlim A parameter similar to that in par, to monitor the limits of the horizontal axis ylim A parameter similar to that in par, to monitor the limits of the vertical axis xlab, ylab Labels for the axes of the R plot
options_SI	For what = "SI" or "all". A list of graphical options: prob_min A numeric value between 0 and 1. The SI distributions explored are only shown from time 0 up to the time t so that each distribution explored has probability < prob_min to be on any time step after t. Defaults to 0.001. col A color or vector of colors used for plotting the SI. Defaults to black. transp A numeric value between 0 and 1 used to monitor transparency of the lines. Defaults to 0.25 xlim A parameter similar to that in par, to monitor the limits of the horizontal axis ylim A parameter similar to that in par, to monitor the limits of the vertical axis xlab, ylab Labels for the axes of the serial interval distribution plot
legend	A boolean (TRUE by default) governing the presence / absence of legends on the plots
...	further arguments passed to other methods (currently unused).

Value

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

Author(s)

Rolina van Gaalen [e-mail] and Anne Cori [e-mail]; S3 method by Thibaut Jombart

See Also

estimate_R, wallinga_teunis and estimate_R_plots

Examples

## 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
                                    )
                      )

## 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)

EpiEstim documentation built on Jan. 7, 2021, 5:10 p.m.