1d:T6be,# Aggregate multiple `` objects into a single `` object. Combine a list of `` objects into a single `` with a mixture distribution [see `distributional::dist_mixture()`]. The aggregated `` returned from `aggregate()` can then be used with the `density()`, `cdf()`, `quantile()` and `generate()` methods for the combined distributions. ```r ## S3 method for class 'multi_epiparameter' aggregate(x, weighting = c("equal", "sample_size", "custom"), ..., weights) ``` ## Arguments - `x`: A `` object. - `weighting`: A `character` string with the type of weighting to use to create the mixture distribution. Options are: `"equal"` for equal weighting across distributions, `"sample_size"` for using the sample size in each `` object to weight the distribution (the sample sizes are normalised), or `"custom"` allows a vector of weights to be passed to the `weights` argument for a custom weighting. - `...`: dots Not used, will warn if extra arguments are passed to function. - `weights`: A `numeric` vector of equal length the number of `` objects passed to `x`. `weights` is only required if `weighting = "custom"`. ## Returns An `` object ## Details The `aggregate()` method requires that all `` objects are parameterised with `` objects (from the `distributional` package). This means that unparameterised (see `is_parameterised()`) or discretised (see `discretise()`) distributions cannot be aggregated and the function will error. ## Examples ```r ebola_si <- epiparameter_db(epi_name = "serial interval", disease = "ebola") aggregate(ebola_si) ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584

aggregate.multi_epiparameter function