plot_obs: Plot posterior predictive distributions
In epidemia: Modeling of Epidemics using Hierarchical Bayesian Models

Description Usage Arguments Value See Also Examples

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'
plot_obs(
  object,
  type,
  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,
  ...
)

spaghetti_obs(
  object,
  type,
  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,
  ...
)

`object`	A fitted model object returned by `epim`. See `epimodel-objects`.
`...`	Additional arguments for `posterior_predict.epimodel`. Examples include `newdata`, which allows predictions or counterfactuals.
`type`	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`.
`groups`	Either `NULL` or a character vector specifying the groups to plot for. Default is `NULL`, which plots all modeled groups.
`dates`	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.
`date_breaks`	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.
`date_format`	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".
`cumulative`	If `TRUE` then cumulative observations are plotted rather than daily. Defaults to `FALSE`.
`by_100k`	If `TRUE`, all quantities are plotted per 100k of population. Only possible if the model used a population adjustment.
`bar`	If `TRUE`, observations are plotted as a bar plot. Otherwise, a scatterplot is used. Defaults to `TRUE`.
`levels`	A numeric vector defining the levels of the plotted credible intervals.
`log`	If `TRUE`, plot quantities on a log10-scale. This argument must be `logical`, and defaults to `FALSE`.
`draws`	The number of sample paths to plot.
`alpha`	Sets transparency of sample paths.
`smooth`	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.

plot_rt, plot_infections, plot_infectious, posterior_predict

data("EuropeCovid2")
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 <- do.call(epim, 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)