knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = "center", fig.width = 7, fig.height = 5 )
To illustrate the trend fitting functionality of i2extras we will use the simulated Ebola Virus Disease (EVD) outbreak data from the outbreaks package.
library(outbreaks) library(incidence2) library(i2extras) raw_dat <- ebola_sim_clean$linelist
For this example we will restrict ourselves to the first 20 weeks of data:
dat <- incidence( raw_dat, date_index = date_of_onset, interval = "week" )[1:20, ] dat plot(dat)
We can use
fit_curve() to fit the data with either a poisson or
negative binomial regression model. The output from this will be a nested
object with class
incidence2_fit which has methods available for both
automatic plotting and the calculation of growth (decay) rates and doubling
out <- fit_curve(dat, model = "poisson", alpha = 0.05) out plot(out) growth_rate(out)
To unnest the data we can use
unnest() (a function that has been reexported
from the tidyr package.
fit_curve() also works nicely with grouped
incidence2 objects. In this
situation, we return a nested tibble with some additional columns that
indicate whether there has been a warning or error during the fitting
or prediction stages.
grouped_dat <- incidence( raw_dat, date_index = date_of_onset, interval = "week", groups = hospital )[1:120, ] grouped_dat out <- fit_curve(grouped_dat, model = "poisson", alpha = 0.05) out # plot with a prediction interval but not a confidence interval plot(out, ci = FALSE, pi=TRUE) growth_rate(out)
We provide helper functions,
help filter the output as necessary.
out <- fit_curve(grouped_dat, model = "negbin", alpha = 0.05) is_warning(out) unnest(is_warning(out), fitting_warning)
We can add a rolling average, across current and previous intervals, to
incidence2 object with the
ra <- add_rolling_average(grouped_dat, before = 2) # group observations with the 2 prior ra unnest(ra, rolling_average) plot(ra, color = "white")
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