plot_rt {epidemia} | R Documentation |

Plots credible intervals and the median from the posterior distribution for the time-varying reproduction rates. The user can control the interval levels (i.e. 30%, 50% etc.) and which groups/regions to plot for. This is a generic function.

```
plot_rt(object, ...)
## S3 method for class 'epimodel'
plot_rt(
object,
groups = NULL,
step = FALSE,
dates = NULL,
date_breaks = "2 weeks",
date_format = "%Y-%m-%d",
levels = c(30, 60, 90),
log = FALSE,
smooth = 1,
...
)
spaghetti_rt(
object,
draws = min(500, posterior_sample_size(object)),
alpha = 1/sqrt(draws),
groups = NULL,
step = FALSE,
dates = NULL,
date_breaks = "2 weeks",
date_format = "%Y-%m-%d",
log = FALSE,
smooth = 1,
...
)
```

`object` |
A fitted model object returned by |

`...` |
Additional unnamed arguments to be passed to |

`groups` |
Either |

`step` |
If |

`dates` |
A length 2 vector of |

`date_breaks` |
A string giving the distance between date tick labels.
Default is |

`date_format` |
This function attempts to coerce the |

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

`log` |
If |

`smooth` |
An integer specifying the window used to smooth the reproduction rates. The
default is |

`draws` |
The number of sample paths to plot. |

`alpha` |
Sets transparency of sample paths. |

A `ggplot`

object which can be further modified.

`plot_obs`

, `plot_infections`

, `plot_infectious`

```
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)
```

[Package *epidemia* version 1.0.0 Index]