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Random walk model

Source: R/model_doc.R
RW.Rd

Random walk 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

  • y_mis: Missing values

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

Priors

The priors are passed as a named list with element sigma specifying priors for the corresponding parameter, where sigma / max_score ~ normal+(x1, x2) and the element sigma of the list is a vector of length two containing x1 and x2. NB: usually x1=0 to define a half-normal distribution (sigma is constraint to be positive) and x2 should be positive.

Default priors

The default prior for sigma translates to a width of the predictive distribution to be at most max_score.

Examples

EczemaModel("RW", max_score = 100, discrete = FALSE)
#> RW model (continuous)
#> max_score = 100 
#> Prior: 
#> - sigma / max_score ~ normal+(0,0.1)