plot_obs {epidemia}R Documentation

Plot posterior predictive distributions


Plots credible intervals and median for the observed data under the posterior predictive distribution, and for a specific observation type. The user can control the interval levels (i.e. 30%, 50% etc.) and the plotted group(s). This is a generic function.


plot_obs(object, ...)

## S3 method for class 'epimodel'
  groups = NULL,
  dates = NULL,
  date_breaks = "2 weeks",
  date_format = "%Y-%m-%d",
  cumulative = FALSE,
  by_100k = FALSE,
  bar = TRUE,
  levels = c(30, 60, 90),
  log = FALSE,

  draws = min(500, posterior_sample_size(object)),
  alpha = 1/sqrt(draws),
  groups = NULL,
  dates = NULL,
  date_breaks = "2 weeks",
  date_format = "%Y-%m-%d",
  cumulative = FALSE,
  by_100k = FALSE,
  bar = TRUE,
  log = FALSE,
  smooth = 1,



A fitted model object returned by epim. See epimodel-objects.


Additional arguments for posterior_predict.epimodel. Examples include newdata, which allows predictions or counterfactuals.


A string specifying the name of the observations to plot. This should match one of the names of the response variables in the obs argument used int the call to epim.


Either NULL or a character vector specifying the groups to plot for. Default is NULL, which plots all modeled groups.


A length 2 vector of Date objects. This defines the start and end dates of the date-range to be plotted. Must be coercible to Date if not NA. If an element of the vector is NA then the default lower/upper limit is used. See examples.


A string giving the distance between date tick labels. Default is "2 weeks". This is passed as the date_breaks argument to scale_x_date. Please see here for details.


This function attempts to coerce the dates argument to a vector of Date objects. date_format is passed as the format argument to as.Date. Default is "%Y-%m-%d".


If TRUE then cumulative observations are plotted rather than daily. Defaults to FALSE.


If TRUE, all quantities are plotted per 100k of population. Only possible if the model used a population adjustment.


If TRUE, observations are plotted as a bar plot. Otherwise, a scatterplot is used. Defaults to TRUE.


A numeric vector defining the levels of the plotted credible intervals.


If TRUE, plot quantities on a log10-scale. This argument must be logical, and defaults to FALSE.


The number of sample paths to plot.


Sets transparency of sample paths.


An integer specifying the window used to smooth the reproduction rates. The default is 1, which corresponds to no smoothing.


A ggplot object which can be further modified.

See Also

plot_rt, plot_infections, plot_infectious, posterior_predict


data <- EuropeCovid2$data
data <- dplyr::filter(data, date > date[which(cumsum(deaths) > 10)[1] - 30])
data <- dplyr::filter(data, date < as.Date("2020-05-05"))

rt <- epirt(
  formula = R(country, date) ~ 0 + (1 + public_events + schools_universities + 
     self_isolating_if_ill + social_distancing_encouraged + lockdown || country) + 
     public_events + schools_universities + self_isolating_if_ill + 
     social_distancing_encouraged + lockdown,
  prior = shifted_gamma(shape=1/6, scale = 1, shift = log(1.05)/6),
  prior_covariance = rstanarm::decov(shape = c(2, rep(0.5, 5)),scale=0.25),
  link = scaled_logit(6.5)

inf <- epiinf(gen = EuropeCovid$si, seed_days = 6)

deaths <- epiobs(
  formula = deaths ~ 1,
  i2o = EuropeCovid2$inf2death,
  prior_intercept = rstanarm::normal(0,0.2),
  link = scaled_logit(0.02)

args <- list(rt=rt, inf=inf, obs=deaths, data=data, seed=12345)
args$group_subset <- c("Italy", "Austria", "Germany")
args$algorithm <- "fullrank"
args$iter <- 1e4
args$tol_rel_obj <- 1e-3

fm <-, args)

# different ways of using plot_rt
p <- plot_rt(fm) # default, plots all groups and dates
p <- plot_rt(fm, dates=c("2020-03-21", NA)) # plot 21 March 2020 onwards
p <- plot_rt(fm, dates=c(NA, "2020-03-20")) # plot up to  20 March 2020
p <- plot_rt(fm, dates=c("2020-03-20", "2020-04-20"))
p <- plot_rt(fm,
         dates=c("2020-03-20", "2020-04-20"),
        date_breaks="1 day") # ticks every day
p <- plot_rt(fm,
       dates=c("2020-20-03", "2020-20-04"),
       date_format="%Y-%d-%m") # (different date format)

# other plotting functions
p <- plot_obs(fm, type = "deaths")
p <- plot_infections(fm)
p <- plot_infectious(fm) 

[Package epidemia version 1.0.0 Index]