Exponential smoothing model

Arguments

max_score: Maximum value that the score can take
discrete: Whether to use a discrete normal distribution. This will be used to check whether the data is discrete or not, and for rounding predictions (cf. testing).
prior: Named list of the model's priors. If NULL, uses the default prior for the model (see default_prior()).

Details

Details of the model are available in the paper.
The model takes as input a continuous score defined between 0 and max_score.
The model is naive as the likelihood is non-truncated and not discretised (when discrete = TRUE). As a result, sampling from the prior predictive distribution can be challenging if the score is near the bounds and the variance is sufficiently large.
For more details see the vignette.

Parameters

sigma: Standard deviation of the random walk
alpha: Smoothing factor
tau: Time constant associated with the smoothing factor
y_mis: Missing values

See list_parameters(model = "Smoothing") for more details.

Priors

The priors are passed as a named list with elements sigma and tau specifying priors for the corresponding parameters. Each element of the list should be a vector of length 2, containing values for x1 and x2, x2 > 0, such as:

sigma / max_score ~ normal+(x1, x2).
tau ~ lognormal(x1, x2).

NB: For sigma, usually x1=0 to define a half-normal distribution since the parameter is constrained to be positive.

Default priors

The default prior for sigma translates to a width of the predictive distribution to be at most max_score.
The default prior for tau assumes it could range from less a 1 to 100 (time units).

Examples

EczemaModel("Smoothing", max_score = 100)
#> Smoothing model (continuous)
#> max_score = 100 
#> Prior: 
#> - sigma / max_score ~ normal+(0,0.1)
#> - tau ~ lognormal(1.2,1.7)