Create an epidist_estimates_data object from a multivariate representation — as_epidist_estimates_data.epidist

This is the only route by which a covariance between a study's reported summaries reaches as_epidist_meta_model(). The covariance comes from draws of the study's parameters, so it is one the study could have computed, and the summaries it covers are fitted with a multivariate normal likelihood rather than as independent observations.

Usage

# S3 method for class 'epidist_multivariate'
as_epidist_estimates_data(
  data,
  study,
  family = NULL,
  moments = c("mean", "sd"),
  probs = numeric(0),
  mvn_id = NULL,
  advise = TRUE,
  ...
)

Arguments

data: The data to convert
study: A string naming the study the draws come from.
family: The distribution the study fitted, one of "lognormal", "gamma" or "weibull", where the draws hold its natural parameters. Defaults to NULL, meaning the draws already hold reported summaries.
moments: Which moments to report, any of "mean" and "sd". Only used where family is given.
probs: A numeric vector of probabilities to report quantiles at. Only used where family is given.
mvn_id: A string identifying the covariance matrix. Defaults to study, and only needs setting where one study contributes more than one multivariate object.
advise: Whether to run the advisory checks of the Checks section and message about the studies they flag. Defaults to TRUE. The list method sets it to FALSE for each element and runs the checks once on the combined object.
...: Study metadata, as documented in as_epidist_estimates_data.data.frame().

Details

Where family is given, the draws hold the natural parameters of a distribution the study fitted. Each draw is pushed through to the summaries the fitted distribution implies, over the range of delays the study could have seen, and the covariance is taken over those. This is the exact version of the delta method epidist_estimates_parameters() applies, and it needs no linearisation.

Where family is NULL, the parameters must already be quantities a study reports. Name them mean, sd, or q followed by a probability, such as q0.25.

Draws over more than one index point are not yet supported for fitting, because the linear predictor would have to vary within one likelihood observation.

Examples

set.seed(1)
draws <- cbind(mean = rnorm(500, 7.5, 0.3), sd = rnorm(500, 3.6, 0.2))
as_epidist_estimates_data(
  as_epidist_multivariate(draws),
  study = "site A",
  trunc_adjusted = TRUE,
  cens_adjusted = 1
)
#> ℹ No `pwindow` column supplied, assuming a censoring window of 1 (daily
#>   reporting) for every study.
#> ℹ No `swindow` column supplied, assuming a censoring window of 1 (daily
#>   reporting) for every study.
#> ℹ No relative_obs_time column supplied, assuming no observation time limit (no
#>   right truncation) for every study.
#> ℹ No max_delay column supplied, using the delay beyond which 1% of the second
#>   moment of a lognormal matched to each study's summaries lies (at least 10 and
#>   at most twenty times the largest reported value, in whole secondary windows)
#>   as the grid cutoff, or five times the largest reported value where nothing
#>   can be matched. Raise it if the delay has a longer tail than that, and lower
#>   it to speed up fitting.
#> # A tibble: 2 × 16
#>   study  type  value    se     n     p pwindow swindow relative_obs_time
#>   <chr>  <chr> <dbl> <dbl> <dbl> <dbl>   <dbl>   <dbl>             <dbl>
#> 1 site A mean   7.51    NA    NA    NA       1       1               Inf
#> 2 site A sd     3.59    NA    NA    NA       1       1               Inf
#> # ℹ 7 more variables: trunc_adjusted <lgl>, trunc_design <chr>,
#> #   cens_adjusted <int>, delay_min <dbl>, growth_rate <dbl>, max_delay <dbl>,
#> #   mvn_id <chr>

Create an `epidist_estimates_data` object from a multivariate representation

Usage

Arguments

Details

See also

Examples