Add natural scale summaries of the delay distribution

Adds the mean and standard deviation of the delay distribution implied by each draw of the distributional parameters, and quantiles of that distribution if probs is given. Analytic solutions are used for the families that have one. Every other family is summarised by simulating delays from it, which works for any family brms can predict from.

Usage

add_summaries(
  data,
  family = NULL,
  probs = NULL,
  method = c("auto", "analytic", "sample"),
  nsim = 1000
)

Arguments

data: A data.frame of draws of the distributional parameters, as returned by delay_parameter_draws().
family: A model fit with epidist(), a brms family, or the name of one, giving the delay distribution. If NULL, the default, the family is taken from data, which delay_parameter_draws() records on it. Some dplyr verbs drop that record, so pass the fit or the family if data has been through one of them.
probs: A numeric vector of probabilities to add quantiles of the delay distribution for. If NULL, the default, no quantiles are added.
method: Either "auto", the default, which uses the analytic solution when there is one and simulates otherwise, "analytic", which errors when there is no analytic solution, or "sample", which always simulates.
nsim: The number of delays to simulate per row of data. Defaults to 1000. Only used when simulating.

Value

The input with mean and sd columns added, and one column per element of probs.

Details

The summaries describe the delay distribution, not the posterior. A row of data holds one draw of the distributional parameters, and the columns added are the mean, standard deviation and quantiles of the delay distribution those parameters define. Summarise the resulting columns across draws to get posterior summaries of them.

Quantile columns are named as in posterior::quantile2(), so probs = 0.05 gives a q5 column.

Simulation adds Monte Carlo error to the summaries. The standard error of the mean is the delay standard deviation divided by the square root of nsim. Simulation is also memory hungry, because it draws nsim delays for every row of data. Reduce the number of rows with epidist_strata(), or the number of draws with the ndraws argument of delay_parameter_draws(), if it is slow.

Examples

draws <- data.frame(mu = c(1.8, 2.0), sigma = c(0.5, 0.4))
add_summaries(draws, family = "lognormal", probs = c(0.05, 0.95))
#>    mu sigma     mean       sd       q5      q95
#> 1 1.8   0.5 6.855149 3.653385 2.657998 13.76910
#> 2 2.0   0.4 8.004469 3.334232 3.826913 14.26689