Smooth noisy incidence data — smooth

Smooth a time series of noisy observations. Currently only LOESS smoothing (smoothing_method = "LOESS") is implemented.

smooth_incidence(
  incidence_data,
  smoothing_method = "LOESS",
  simplify_output = TRUE,
  ...
)

Arguments

incidence_data	An object containing incidence data through time. It can either be: A list with two elements: A numeric vector named `values`: the incidence recorded on consecutive time steps. An integer named `index_offset`: the offset, counted in number of time steps, by which the first value in `values` is shifted compared to a reference time step This parameter allows one to keep track of the date of the first value in `values` without needing to carry a `date` column around. A positive offset means `values` are delayed in the future compared to the reference values. A negative offset means the opposite. A numeric vector. The vector corresponds to the `values` element descrived above, and `index_offset` is implicitely zero. This means that the first value in `incidence_data` is associated with the reference time step (no shift towards the future or past).
smoothing_method	string. Method used to smooth the original incidence data. Available options are: 'LOESS', implemented in `.smooth_LOESS`
simplify_output	boolean. Return a numeric vector instead of module output object if output offset is zero?
...	Arguments passed on to `.smooth_LOESS` `data_points_incl` integer. Size of the window used in the LOESS algorithm. The `span` parameter passed to `loess` is computed as the ratio of `data_points_incl` and the number of time steps in the input data. `degree` integer. LOESS degree. Must be 0, 1 or 2. `initial_Re_estimate_window` integer. In order to help with the smoothing, the function extends the data back in time, padding with values obtained by assuming a constant Re. This parameter represents the number of timesteps in the beginning of `incidence_input` to take into account when computing the average initial Re.

Value

A list with two elements:

A numeric vector named values: the result of the computations on the input data.
An integer named index_offset: the offset, counted in number of time steps, by which the result is shifted compared to an index_offset of 0. This parameter allows one to keep track of the date of the first value in values without needing to carry a date column around. A positive offset means values are delayed in the future compared to the reference values. A negative offset means the opposite. Note that the index_offset of the output of the function call accounts for the (optional) index_offset of the input.

If index_offset is 0 and simplify_output = TRUE, the index_offset is dropped and the values element is returned as a numeric vector.

Examples

## Basic usage of smooth_incidence

smoothed_incidence_1 <- smooth_incidence(
  incidence_data = HK_incidence_data$case_incidence,
  smoothing_method = "LOESS"
)


## Advanced usage of smooth_incidence
# Smoothing the incidence using a LOESS window of 15 days, fitting polynomials 
# of degree 2 in the LOESS algorithm, and averaging the initial Re estimate over
# the first 7 days. 

smoothed_incidence_2 <- smooth_incidence(
  incidence_data = HK_incidence_data$case_incidence,
  smoothing_method = "LOESS",
  simplify_output = FALSE,
  data_points_incl = 15, 
  degree = 2,
  initial_Re_estimate_window = 7
)