Package 'epidist'

Description

Add natural scale mean and standard deviation parameters

Usage

add_mean_sd(data, ...)

Arguments

See Also

Other postprocess: add_mean_sd.default(), add_mean_sd.gamma_samples(), add_mean_sd.lognormal_samples(), predict_delay_parameters()

Description

Default method for add natural scale parameters

Usage

## Default S3 method:
add_mean_sd(data, ...)

Arguments

See Also

Other postprocess: add_mean_sd(), add_mean_sd.gamma_samples(), add_mean_sd.lognormal_samples(), predict_delay_parameters()

Description

Again, mu and shape here are the distributional parameters of brms.

Usage

## S3 method for class 'gamma_samples'
add_mean_sd(data, ...)

Arguments

See Also

Other postprocess: add_mean_sd(), add_mean_sd.default(), add_mean_sd.lognormal_samples(), predict_delay_parameters()

Description

Note that the input parameters here are mu and sigma, corresponding to the distributional parameters used by brms for the lognormal family.

Usage

## S3 method for class 'lognormal_samples'
add_mean_sd(data, ...)

Arguments

See Also

Other postprocess: add_mean_sd(), add_mean_sd.default(), add_mean_sd.gamma_samples(), predict_delay_parameters()

Description

Creates an epidist_aggregate_data object from various input formats. This is useful when working with pre-aggregated data where each row represents multiple identical observations. See the specific methods for details on supported input formats and usage examples.

Usage

as_epidist_aggregate_data(data, ...)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), as_epidist_aggregate_data.epidist_linelist_data(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Description

This method takes a data.frame containing event dates (primary/secondary event dates and observation date) along with counts and creates an epidist_aggregate_data object. This format is useful when working with pre-aggregated data where each row represents multiple identical observations with the count stored in a specified column. Internally it makes use of as_epidist_linelist_data.data.frame() to convert the data to a linelist format before adding the count column. See the other methods for other data input options.

Usage

## S3 method for class 'data.frame'
as_epidist_aggregate_data(
  data,
  n = NULL,
  pdate_lwr = NULL,
  sdate_lwr = NULL,
  pdate_upr = NULL,
  sdate_upr = NULL,
  obs_date = NULL,
  ...
)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.default(), as_epidist_aggregate_data.epidist_linelist_data(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  )

Description

This method takes vectors of event times (primary/secondary event times and observation time) along with counts and creates an epidist_aggregate_data object. This format is useful when working with pre-aggregated data where each row represents multiple identical observations with the count stored in the n column. Internally it makes use of as_epidist_linelist_data.default() to convert the data to a linelist format before adding the count column. See the other methods for other data input options.

Usage

## Default S3 method:
as_epidist_aggregate_data(
  data,
  n = NULL,
  ptime_upr = NULL,
  stime_lwr = NULL,
  stime_upr = NULL,
  obs_time = NULL,
  ...
)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.epidist_linelist_data(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Examples

as_epidist_aggregate_data(
  data = c(1, 2, 3),
  ptime_upr = c(2, 3, 4),
  stime_lwr = c(3, 4, 5),
  stime_upr = c(4, 5, 6),
  obs_time = c(5, 6, 7),
  n = c(1, 2, 3)
)

Description

This method takes an epidist_linelist_data object (see as_epidist_linelist_data()) and aggregates it by counting unique combinations of the required time variables (primary/secondary event times and observation time) and any additional variables specified in by. The result is a more compact representation of the same data where each row represents multiple identical observations with the count stored in the n column.

Usage

## S3 method for class 'epidist_linelist_data'
as_epidist_aggregate_data(data, by = NULL, ...)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Examples

# Default stratification by required time variables only
sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data()

# Additional stratification by other variables
sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data(by = "age")

Description

Creates an epidist_latent_model object from various input formats. This enables fitting latent variable models for epidemiological delays using epidist(), as described in Park et al. (2024) and Charniga et al. (2024) The latent model approach accounts for double interval censoring and right truncation in delay data.

Usage

as_epidist_latent_model(data, ...)

Arguments

References

• Park et al. (2024)

• Charniga et al. (2024)

See Also

Other latent_model: as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Description

This method converts aggregate data to a latent model format by first converting it to linelist format using as_epidist_linelist_data.epidist_aggregate_data() and then passing it to as_epidist_latent_model.epidist_linelist_data(). This ensures that the counts in the aggregate data are properly expanded into individual observations before fitting the latent model.

Usage

## S3 method for class 'epidist_aggregate_data'
as_epidist_latent_model(data, ...)

Arguments

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  ) |>
  as_epidist_latent_model()

Description

This method takes an epidist_linelist_data object and converts it to a format suitable for fitting latent variable models. It calculates key variables needed for the latent variable method described in Park et al. (2024) and Charniga et al. (2024). This approach adjusts for double interval censoring and right truncation in the data.

Usage

## S3 method for class 'epidist_linelist_data'
as_epidist_latent_model(data, ...)

Arguments

References

• Park et al. (2024)

• Charniga et al. (2024)

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_latent_model()

Description

Creates an epidist_linelist_data object from various input formats. This is useful when working with individual-level data where each row represents a single observation with primary and secondary event times. See the specific methods for details on supported input formats and usage examples.

Usage

as_epidist_linelist_data(data, ...)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.default(), as_epidist_linelist_data.epidist_aggregate_data(), assert_epidist.epidist_linelist_data(), is_epidist_linelist_data(), new_epidist_linelist_data()

Description

This method takes a data.frame containing event dates (primary/secondary event dates and observation date) and creates an epidist_linelist_data object. This format is useful when working with individual-level data where each row represents a single observation. Internally it converts dates to numeric times relative to the earliest primary event date and uses as_epidist_linelist_data.default() to create the final object. See the other methods for other data input options.

Usage

## S3 method for class 'data.frame'
as_epidist_linelist_data(
  data,
  pdate_lwr = NULL,
  sdate_lwr = NULL,
  pdate_upr = NULL,
  sdate_upr = NULL,
  obs_date = NULL,
  ...
)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.default(), as_epidist_linelist_data.epidist_aggregate_data(), assert_epidist.epidist_linelist_data(), is_epidist_linelist_data(), new_epidist_linelist_data()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  )

Description

This method takes vectors of event times (primary/secondary event times and observation time) and creates an epidist_linelist_data object. This format is useful when working with individual-level data where each row represents a single observation. See the other methods for other data input options.

Usage

## Default S3 method:
as_epidist_linelist_data(
  data,
  ptime_upr = NULL,
  stime_lwr = NULL,
  stime_upr = NULL,
  obs_time = NULL,
  ...
)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.epidist_aggregate_data(), assert_epidist.epidist_linelist_data(), is_epidist_linelist_data(), new_epidist_linelist_data()

Examples

as_epidist_linelist_data(
  data = c(1, 2, 3),
  ptime_upr = c(2, 3, 4),
  stime_lwr = c(3, 4, 5),
  stime_upr = c(4, 5, 6),
  obs_time = c(5, 6, 7)
)

Description

This method expands an epidist_aggregate_data object into individual observations by uncounting the n column, then converts it to linelist format using as_epidist_linelist_data.data.frame().

Usage

## S3 method for class 'epidist_aggregate_data'
as_epidist_linelist_data(data, ...)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.default(), assert_epidist.epidist_linelist_data(), is_epidist_linelist_data(), new_epidist_linelist_data()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  ) |>
  as_epidist_linelist_data()

Description

Creates an epidist_marginal_model object from various input formats. This enables fitting marginal models for epidemiological delays using epidist(). The marginal model approach uses the likelihood from the primarycensored package to efficiently handle censoring in both primary and secondary events as well as truncation due to observation times. See the specific methods as_epidist_marginal_model.epidist_linelist_data() and as_epidist_marginal_model.epidist_aggregate_data() for details on supported input formats and usage examples.

Usage

as_epidist_marginal_model(data, ...)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Description

This method converts aggregate data to a marginal model format by passing it to as_epidist_marginal_model.epidist_linelist_data() with the n column used as weights. This ensures that the likelihood is weighted by the counts in the aggregate data.

Usage

## S3 method for class 'epidist_aggregate_data'
as_epidist_marginal_model(data, obs_time_threshold = 2, ...)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  ) |>
  as_epidist_marginal_model()

Description

This method converts linelist data to a marginal model format by calculating delays between primary and secondary events, along with observation times and censoring windows. The likelihood used is imported from the primarycensored package which handles censoring in both primary and secondary events as well as truncation due to observation times. In principle, this method should be more accurate and more computationally efficient than the latent model (as_epidist_latent_model()) approach in most setting except when the number of unique strata approaches the number of observations.

Usage

## S3 method for class 'epidist_linelist_data'
as_epidist_marginal_model(data, obs_time_threshold = 2, weight = NULL, ...)

Arguments

Details

When a formula is specified in epidist(), the data will be transformed using epidist_transform_data_model.epidist_marginal_model() to prepare it for model fitting. This transformation summarises the data by counting unique combinations of delays, observation times, censoring windows and any variables in the model formula.

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_marginal_model()

Description

Creates an epidist_naive_model object from various input formats. This enables fitting naive models for epidemiological delays using epidist(). The naive model approach ignores censoring and truncation in the data, using only the lower bounds of the intervals as point estimates. This is the simplest approach but may lead to biased estimates if there is substantial censoring or truncation in the data.

Usage

as_epidist_naive_model(data, ...)

Arguments

See Also

Other naive_model: as_epidist_naive_model.epidist_aggregate_data(), as_epidist_naive_model.epidist_linelist_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Description

This method converts aggregate data to a naive model format by passing it to as_epidist_naive_model.epidist_linelist_data() with the n column used as weights. This ensures that the likelihood is weighted by the counts in the aggregate data.

Usage

## S3 method for class 'epidist_aggregate_data'
as_epidist_naive_model(data, ...)

Arguments

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_linelist_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Examples

sierra_leone_ebola_data |>
  dplyr::count(date_of_symptom_onset, date_of_sample_tested) |>
  as_epidist_aggregate_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested",
    n = "n"
  ) |>
  as_epidist_naive_model()

Description

This method converts linelist data to a naive model format by calculating delays between primary and secondary events to enable model fitting in epidist(). If the input data contains an n column (e.g. from aggregated data), the likelihood will be weighted by these counts.

Usage

## S3 method for class 'epidist_linelist_data'
as_epidist_naive_model(data, weight = NULL, ...)

Arguments

Details

When a formula is specified in epidist(), the data will be transformed using epidist_transform_data_model.epidist_naive_model() to prepare it for model fitting. This transformation summarises the data by counting unique combinations of delays and any variables in the model formula.

The naive model is the simplest approach but ignores censoring and truncation in the data by using only lower bounds as point estimates. For data with substantial censoring or truncation, consider using as_epidist_latent_model() or as_epidist_marginal_model() which properly account for these features.

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_naive_model()

Description

Validation for epidist objects

Usage

assert_epidist(data, ...)

Arguments

Value

NULL invisibly

Description

Assert validity of epidist_aggregate_data objects

Usage

## S3 method for class 'epidist_aggregate_data'
assert_epidist(data, ...)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), as_epidist_aggregate_data.epidist_linelist_data(), is_epidist_aggregate_data(), new_epidist_aggregate_data()

Examples

sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data() |>
  assert_epidist()

Description

Assert validity of epidist_linelist_data objects

Usage

## S3 method for class 'epidist_linelist_data'
assert_epidist(data, ...)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.default(), as_epidist_linelist_data.epidist_aggregate_data(), is_epidist_linelist_data(), new_epidist_linelist_data()

Description

Fit epidemiological delay distributions using a brms interface

Usage

epidist(
  data,
  formula = mu ~ 1,
  family = lognormal(),
  prior = NULL,
  merge_priors = TRUE,
  fn = brms::brm,
  ...
)

Arguments

Examples

fit <- sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data() |>
  as_epidist_marginal_model() |>
  epidist(chains = 2, cores = 2, refresh = ifelse(interactive(), 250, 0))

summary(fit)

Description

This function computes diagnostics to assess the quality of a fitted model. When the fitting algorithm used is "sampling" (HMC) then the output of epidist_diagnostics is a data.frame containing:

• time: the total time taken to fit all chains

• samples: the total number of samples across all chains

• max_rhat: the highest value of the Gelman-Rubin statistic

• divergent_transitions: the total number of divergent transitions

• per_divergent_transitions: the proportion of samples which had divergent transitions

• max_treedepth: the highest value of the treedepth HMC parameter

• no_at_max_treedepth: the number of samples which attained the max_treedepth

• per_at_max_treedepth: the proportion of samples which attained the max_treedepth

Usage

epidist_diagnostics(fit)

Arguments

Details

When the fitting algorithm is not "sampling" (see brms::brm() for other possible algorithms) then diagnostics are yet to be implemented.

Examples

fit <- sierra_leone_ebola_data |>
  as_epidist_linelist_data(
    pdate_lwr = "date_of_symptom_onset",
    sdate_lwr = "date_of_sample_tested"
  ) |>
  as_epidist_aggregate_data() |>
  as_epidist_marginal_model() |>
  epidist(chains = 2, cores = 2, refresh = ifelse(interactive(), 250, 0))
epidist_diagnostics(fit)

Description

This function is used within epidist() to create a model specific custom brms family object. This custom family is passed to brms. It is unlikely that as a user you will need this function, but we export it nonetheless to be transparent about what happens inside of a call to epidist().

Usage

epidist_family(data, family = lognormal(), ...)

Arguments

See Also

Other family: epidist_family_model(), epidist_family_model.default(), epidist_family_param(), epidist_family_param.default()

Description

The model-specific parts of an epidist_family() call

The model-specific parts of an epidist_formula() call

Usage

epidist_family_model(data, family, ...)

epidist_formula_model(data, formula, ...)

Arguments

See Also

Other family: epidist_family(), epidist_family_model.default(), epidist_family_param(), epidist_family_param.default()

Other formula: epidist_formula(), epidist_formula_model.default()

Description

Default method for defining a model specific family

Usage

## Default S3 method:
epidist_family_model(data, family, ...)

Arguments

See Also

Other family: epidist_family(), epidist_family_model(), epidist_family_param(), epidist_family_param.default()

Description

Create the model-specific component of an epidist custom family

Usage

## S3 method for class 'epidist_latent_model'
epidist_family_model(data, family, ...)

Arguments

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Description

Create the model-specific component of an epidist custom family

Usage

## S3 method for class 'epidist_marginal_model'
epidist_family_model(data, family, ...)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Description

Reparameterise an epidist family to align brms and Stan

Usage

epidist_family_param(family, ...)

Arguments

See Also

Other family: epidist_family(), epidist_family_model(), epidist_family_model.default(), epidist_family_param.default()

Description

This function extracts the Stan parameterisation for a given brms family by creating a dummy model and parsing its Stan code. It looks for the log probability density function (lpdf) call in the Stan code and extracts the parameter order used by Stan. This is needed because brms and Stan may use different parameter orderings for the same distribution.

Usage

## Default S3 method:
epidist_family_param(family, ...)

Arguments

Details

The function works by:

1. Creating a minimal dummy model using the specified family

2. Extracting the Stan code for this model

3. Finding the lpdf function call for the family

4. Parsing out the parameter ordering used in Stan

5. Adding this as the param element to the family object

Value

The input family object with an additional param element containing the Stan parameter ordering as a string

See Also

Other family: epidist_family(), epidist_family_model(), epidist_family_model.default(), epidist_family_param()

Description

This function contains brms prior distributions which are specific to particular likelihood families e.g. brms::lognormal().

Usage

epidist_family_prior(family, ...)

Arguments

See Also

Other prior: epidist_family_prior.default(), epidist_family_prior.lognormal(), epidist_model_prior(), epidist_model_prior.default(), epidist_prior()

Description

By default, we do not return any family specific prior distributions.

Usage

## Default S3 method:
epidist_family_prior(family, formula, ...)

Arguments

See Also

Other prior: epidist_family_prior(), epidist_family_prior.lognormal(), epidist_model_prior(), epidist_model_prior.default(), epidist_prior()

Description

We suggest priors to overwrite the brms defaults for the lognormal family.

Usage

## S3 method for class 'lognormal'
epidist_family_prior(family, formula, ...)

Arguments

See Also

Other prior: epidist_family_prior(), epidist_family_prior.default(), epidist_model_prior(), epidist_model_prior.default(), epidist_prior()

Description

This function is used within epidist() to create the formula object passed to brms. It is unlikely that as a user you will need this function, but we export it nonetheless to be transparent about what exactly is happening inside of a call to epidist().

Usage

epidist_formula(data, family, formula, ...)

Arguments

See Also

Other formula: epidist_family_model(), epidist_formula_model.default()

Description

Default method for defining a model specific formula

Usage

## Default S3 method:
epidist_formula_model(data, formula, ...)

Arguments

See Also

Other formula: epidist_family_model(), epidist_formula()

Description

Define the model-specific component of an epidist custom formula for the latent model

Usage

## S3 method for class 'epidist_latent_model'
epidist_formula_model(data, formula, ...)

Arguments

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Description

Define the model-specific component of an epidist custom formula for the marginal model

Usage

## S3 method for class 'epidist_marginal_model'
epidist_formula_model(data, formula, ...)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Description

Define the model-specific component of an epidist custom formula for the naive model

Usage

## S3 method for class 'epidist_naive_model'
epidist_formula_model(data, formula, ...)

Arguments

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), as_epidist_naive_model.epidist_linelist_data(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Description

This function creates a log likelihood function that calculates the marginal likelihood for a single observation by integrating over the latent primary and secondary event windows. The integration is performed numerically using primarycensored::dpcens() which handles the double censoring and truncation of the delay distribution.

Usage

epidist_gen_log_lik(family)

Arguments

Details

The marginal likelihood accounts for uncertainty in both the primary and secondary event windows by integrating over their possible values, weighted by their respective uniform distributions.

Value

A function that calculates the marginal log likelihood for a single observation. The prep object must have the following variables:

• vreal1: relative observation time

• vreal2: primary event window

• vreal3: secondary event window

See Also

brms::log_lik() for details on the brms log likelihood interface.

Other gen: epidist_gen_posterior_epred(), epidist_gen_posterior_predict()

Description

This function creates a function that calculates the expected value of the posterior predictive distribution for a latent model. The returned function takes a prep argument (from brms) and returns posterior expected values. This is used internally by brms::posterior_epred() to calculate expected values for latent models.

Usage

epidist_gen_posterior_epred(family)

Arguments

Value

A function that takes a prep argument from brms and returns a matrix of posterior expected values, with one row per posterior draw and one column per observation.

See Also

brms::posterior_epred() for details on how this is used within brms.

Other gen: epidist_gen_log_lik(), epidist_gen_posterior_predict()

Description

This function creates a function that draws from the posterior predictive distribution for a latent model using primarycensored::rpcens() to handle censoring and truncation. The returned function takes a prep argument from brms and returns posterior predictions. This is used internally by brms::posterior_predict() to generate predictions for latent models.

Usage

epidist_gen_posterior_predict(family)

Arguments

Value

A function that takes a prep argument from brms and returns a matrix of posterior predictions, with one row per posterior draw and one column per observation. The prep object must have the following variables:

• vreal1: relative observation time

• vreal2: primary event window

• vreal3: secondary event window

See Also

brms::posterior_predict() for details on how this is used within brms, primarycensored::rpcens() for details on the censoring approach

Other gen: epidist_gen_log_lik(), epidist_gen_posterior_epred()

Description

This function contains brms prior distributions which are specific to particular epidist models e.g. the latent_lognormal model.

Usage

epidist_model_prior(data, ...)

Arguments

See Also

Other prior: epidist_family_prior(), epidist_family_prior.default(), epidist_family_prior.lognormal(), epidist_model_prior.default(), epidist_prior()

Description

By default, we do not return any model specific prior distributions.

Usage

## Default S3 method:
epidist_model_prior(data, formula, ...)

Arguments

See Also

Other prior: epidist_family_prior(), epidist_family_prior.default(), epidist_family_prior.lognormal(), epidist_model_prior(), epidist_prior()

Description

Defines prior distributions for the latent model parameters pwindow_raw and swindow_raw which control the width of the observation windows.

Usage

## S3 method for class 'epidist_latent_model'
epidist_model_prior(data, formula, ...)

Arguments

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), is_epidist_latent_model(), new_epidist_latent_model()

Description

This function combines model specific prior distributions from epidist_model_prior(), family specific prior distributions from epidist_family_prior(), and user provided prior distributions into a single set of custom priors. Each element overwrites previous elements, such that user provided prior distributions have the highest priority. If a user prior distribution is provided which is not included in the model, a warning will be shown.

Usage

epidist_prior(
  data,
  family,
  formula,
  prior,
  merge = TRUE,
  enforce_presence = FALSE
)

Arguments

Details

Note that the matching of priors is imperfect as it does not use brms' internal prior matching functionality. For example, it cannot distinguish between a prior for all coefficients (class = "b") and a prior for a specific coefficient (class = "b" and coef specified).

Value

A brmsprior object containing the combined custom prior distributions.

See Also

Other prior: epidist_family_prior(), epidist_family_prior.default(), epidist_family_prior.lognormal(), epidist_model_prior(), epidist_model_prior.default()

Description

This function is used within epidist() to create any custom Stan code which is injected into brms via the stanvars argument. It is unlikely that as a user you will need this function, but we export it nonetheless to be transparent about what exactly is happening inside of a call to epidist().

Usage

epidist_stancode(data, ...)

Arguments

See Also

Other stan: epidist_stancode.default()

Description

Default method for defining model specific Stan code

Usage

## Default S3 method:
epidist_stancode(data, ...)

Arguments

See Also

Other stan: epidist_stancode()

Description

This function is used within epidist() to transform data before passing to brms. It is unlikely that as a user you will need this function, but we export it nonetheless to be transparent about what happens inside of a call to epidist().

Usage

epidist_transform_data(data, family, formula, ...)

Arguments

See Also

Other transform_data: epidist_transform_data_model(), epidist_transform_data_model.default()

Description

The model-specific parts of an epidist_transform_data() call

Usage

epidist_transform_data_model(data, family, formula, ...)

Arguments

See Also

Other transform_data: epidist_transform_data(), epidist_transform_data_model.default()

Description

Default method for transforming data for a model

Usage

## Default S3 method:
epidist_transform_data_model(data, family, formula, ...)

Arguments

See Also

Other transform_data: epidist_transform_data(), epidist_transform_data_model()

Description

This method transforms data into the format required by the marginal model by:

1. Identifying required columns for the marginal model

2. Summarising the data by counting unique combinations of these columns and any variables in the model formula using .summarise_n_by_formula()

3. Converting the summarised data to a marginal model object using new_epidist_marginal_model()

4. Informing the user about any data aggregation that occurred using .inform_data_summarised()

Usage

## S3 method for class 'epidist_marginal_model'
epidist_transform_data_model(data, family, formula, ...)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), is_epidist_marginal_model(), new_epidist_marginal_model()

Description

This method transforms data into the format required by the naive model by:

1. Identifying required columns for the naive model

2. Summarising the data by counting unique combinations of these columns and any variables in the model formula using .summarise_n_by_formula()

3. Converting the summarised data to a naive model object using new_epidist_naive_model()

4. Informing the user about any data aggregation that occurred using .inform_data_summarised()

Usage

## S3 method for class 'epidist_naive_model'
epidist_transform_data_model(data, family, formula, ...)

Arguments

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), as_epidist_naive_model.epidist_linelist_data(), epidist_formula_model.epidist_naive_model(), is_epidist_naive_model(), new_epidist_naive_model()

Description

Check if data has the epidist_aggregate_data class

Usage

is_epidist_aggregate_data(data, ...)

Arguments

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), as_epidist_aggregate_data.epidist_linelist_data(), assert_epidist.epidist_aggregate_data(), new_epidist_aggregate_data()

Description

Check if data has the epidist_latent_model class

Usage

is_epidist_latent_model(data)

Arguments

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), new_epidist_latent_model()

Description

Check if data has the epidist_linelist_data class

Usage

is_epidist_linelist_data(data, ...)

Arguments

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.default(), as_epidist_linelist_data.epidist_aggregate_data(), assert_epidist.epidist_linelist_data(), new_epidist_linelist_data()

Description

Check if data has the epidist_marginal_model class

Usage

is_epidist_marginal_model(data)

Arguments

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), new_epidist_marginal_model()

Description

Check if data has the epidist_naive_model class

Usage

is_epidist_naive_model(data)

Arguments

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), as_epidist_naive_model.epidist_linelist_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), new_epidist_naive_model()

Description

Class constructor for epidist_aggregate_data objects

Usage

new_epidist_aggregate_data(data)

Arguments

Value

An object of class epidist_aggregate_data

See Also

Other aggregate_data: as_epidist_aggregate_data(), as_epidist_aggregate_data.data.frame(), as_epidist_aggregate_data.default(), as_epidist_aggregate_data.epidist_linelist_data(), assert_epidist.epidist_aggregate_data(), is_epidist_aggregate_data()

Examples

df <- new_epidist_aggregate_data(data.frame())
class(df)

Description

Class constructor for epidist_latent_model objects

Usage

new_epidist_latent_model(data, ...)

Arguments

Value

An object of class epidist_latent_model

See Also

Other latent_model: as_epidist_latent_model(), as_epidist_latent_model.epidist_aggregate_data(), as_epidist_latent_model.epidist_linelist_data(), epidist_family_model.epidist_latent_model(), epidist_formula_model.epidist_latent_model(), epidist_model_prior.epidist_latent_model(), is_epidist_latent_model()

Description

Class constructor for epidist_linelist_data objects

Usage

new_epidist_linelist_data(data)

Arguments

Value

An object of class epidist_linelist_data

See Also

Other linelist_data: as_epidist_linelist_data(), as_epidist_linelist_data.data.frame(), as_epidist_linelist_data.default(), as_epidist_linelist_data.epidist_aggregate_data(), assert_epidist.epidist_linelist_data(), is_epidist_linelist_data()

Description

Class constructor for epidist_marginal_model objects

Usage

new_epidist_marginal_model(data)

Arguments

Value

An object of class epidist_marginal_model

See Also

Other marginal_model: as_epidist_marginal_model(), as_epidist_marginal_model.epidist_aggregate_data(), as_epidist_marginal_model.epidist_linelist_data(), epidist_family_model.epidist_marginal_model(), epidist_formula_model.epidist_marginal_model(), epidist_transform_data_model.epidist_marginal_model(), is_epidist_marginal_model()

Description

Class constructor for epidist_naive_model objects

Usage

new_epidist_naive_model(data)

Arguments

Value

An object of class epidist_naive_model.

See Also

Other naive_model: as_epidist_naive_model(), as_epidist_naive_model.epidist_aggregate_data(), as_epidist_naive_model.epidist_linelist_data(), epidist_formula_model.epidist_naive_model(), epidist_transform_data_model.epidist_naive_model(), is_epidist_naive_model()

Description

Extract samples of the delay distribution parameters

Usage

predict_delay_parameters(fit, newdata = NULL, ...)

predict_dpar(fit, newdata = NULL, ...)

Arguments

See Also

Other postprocess: add_mean_sd(), add_mean_sd.default(), add_mean_sd.gamma_samples(), add_mean_sd.lognormal_samples()

Description

Linelist data for the Ebola virus collected in Sierra Leone. If you use this data in your work, please cite the corresponding paper.

Usage

sierra_leone_ebola_data

Format

A tibble with 8,358 rows and 8 columns:

id

Unique identification number for the case

name

Name as character, omitted

age

Age as numeric

sex

Sex as character, either "F", "M" or NA

date_of_symptom_onset

The date symptoms began

date_of_sample_tested

The date the sample was tested

district

The district (ADM2)

chiefdom

The chiefdom (ADM3)

Source

https://www.pnas.org/doi/full/10.1073/pnas.1518587113

Description

This function simulates cases from an exponential distribution. The user may specify the rate parameter r, the sample size, and the upper bound of the survival time. If the rate parameter is 0, then this function defaults to the uniform distribution.

Usage

simulate_exponential_cases(r = 0.2, sample_size = 10000, seed, t = 30)

Arguments

Value

A data.frame with two columns: case (case number) and ptime (primary event time).

See Also

Other simulate: simulate_gillespie(), simulate_secondary(), simulate_uniform_cases()

Description

This function simulates cases from an stochastic SIR model. The user may specify the initial epidemic growth rate $r$, the rate of recovery gamma $\gamma$, the initial number of infected cases $I_0$, and the total population size $N$.

Usage

simulate_gillespie(r = 0.2, gamma = 1/7, I0 = 50, N = 10000, seed)

Arguments

Value

A data.frame with two columns: case (case number) and ptime (primary event time).

See Also

Other simulate: simulate_exponential_cases(), simulate_secondary(), simulate_uniform_cases()

Description

This function simulates secondary events based on a given delay distribution. The input dataset should have the primary event times in a column named ptime.

Usage

simulate_secondary(data, dist = rlnorm, ...)

Arguments

Value

A data.frame that augments data with two new columns: delay (secondary event latency) and stime (the time of the secondary event).

See Also

Other simulate: simulate_exponential_cases(), simulate_gillespie(), simulate_uniform_cases()

Description

This function simulates cases from a uniform distribution, where the primary event times are uniformly distributed between 0 and t.

Usage

simulate_uniform_cases(sample_size = 1000, t = 60)

Arguments

Value

A data.frame with two columns: case (case number) and ptime (primary event time).

See Also

Other simulate: simulate_exponential_cases(), simulate_gillespie(), simulate_secondary()