Skip to contents

Prepare data for primarycensored Stan model

Source: R/pcd_cmdstan_model.R
pcd_as_stan_data.Rd

This function takes in delay data and prepares it for use with the primarycensored Stan model.

Usage

pcd_as_stan_data(
  data,
  delay = "delay",
  delay_upper = "delay_upper",
  n = "n",
  pwindow = "pwindow",
  start_relative_obs_time = "start_relative_obs_time",
  relative_obs_time = "relative_obs_time",
  dist_id,
  primary_id,
  param_bounds,
  primary_param_bounds,
  priors,
  primary_priors,
  compute_log_lik = FALSE,
  use_reduce_sum = FALSE,
  truncation_check_multiplier = 2,
  dist_options = NULL
)

Arguments

data

A data frame containing the delay data.

delay

Column name for observed delays (default: "delay")

delay_upper

Column name for upper bound of delays (default: "delay_upper")

n

Column name for count of observations (default: "n")

pwindow

Column name for primary window (default: "pwindow")

start_relative_obs_time

Column name for start of relative observation time, used as the lower truncation point L. Values may be any finite real number (including negatives) or -Inf to indicate no lower truncation. If the column is not present in data, L = -Inf is assumed for all observations. (default: "start_relative_obs_time")

relative_obs_time

Column name for relative observation time, used as the upper truncation point D. Values may be any finite real number (including negatives, paired with a smaller start_relative_obs_time) or +Inf to indicate no upper truncation. (default: "relative_obs_time")

dist_id

Integer identifying the delay distribution: You can use pcd_stan_dist_id() to get the dist ID for a distribution or look at the pcd_distributions data set.

primary_id

Integer identifying the primary distribution: You can use pcd_stan_dist_id() to get the primary dist ID for a distribution (make sure to select the "primary" type) or look at the pcd_primary_distributions data set.

param_bounds

A list with elements lower and upper, each a numeric vector specifying bounds for the delay distribution parameters.

primary_param_bounds

A list with elements lower and upper, each a numeric vector specifying bounds for the primary distribution parameters.

priors

A list with elements location and scale, each a numeric vector specifying priors for the delay distribution parameters.

primary_priors

A list with elements location and scale, each a numeric vector specifying priors for the primary distribution parameters.

compute_log_lik

Logical; compute log likelihood? (default: FALSE)

use_reduce_sum

Logical; use reduce_sum for performance? (default: FALSE)

truncation_check_multiplier

Numeric multiplier to use for checking if the truncation time D is appropriate relative to the maximum delay for each unique D value. Set to NULL to skip the check. Default is 2.

dist_options

Optional list carrying the shape of a non-parametric delay. When dist_id is one of 26 (step CDF, Dirichlet prior on the PMF), 27 (step CDF, random walk on the logit hazards) or 28 (step CDF, IID logit random effects on the hazards), this list must be supplied and must contain:

  • K: integer, the number of bins.

  • boundaries: numeric vector of length K + 1.

Priors and param_bounds follow the same channel as the parametric path: pass them through priors and param_bounds. The semantics of priors depend on dist_id:

  • dist_id = 26: priors$scale is the length-K Dirichlet concentration vector; priors$location is unused.

  • dist_id = 27 or 28: priors$location = c(alpha_mean, log_sigma_mean) and priors$scale = c(alpha_sd, log_sigma_sd).

param_bounds is unused for the non-parametric paths; pass list(lower = numeric(0), upper = numeric(0)). If priors is empty when a non-parametric dist_id is given, sensible defaults are applied (Dirichlet(1, ..., 1) for dist_id = 26; N(0, 5) on alpha and N(0, 1) on log_sigma for 27 and 28).

Value

A list containing the data formatted for use with pcd_cmdstan_model()

See also

Modelling wrappers for external fitting packages fitdistdoublecens(), pcd_cmdstan_model()

Examples

data <- data.frame(
  delay = c(1, 2, 3),
  delay_upper = c(2, 3, 4),
  n = c(10, 20, 15),
  pwindow = c(1, 1, 2),
  relative_obs_time = c(10, 10, 10)
)
stan_data <- pcd_as_stan_data(
  data,
  dist_id = 1,
  primary_id = 1,
  param_bounds = list(lower = c(0, 0), upper = c(10, 10)),
  primary_param_bounds = list(lower = numeric(0), upper = numeric(0)),
  priors = list(location = c(1, 1), scale = c(1, 1)),
  primary_priors = list(location = numeric(0), scale = numeric(0))
)
#> The truncation time D (10) is larger than 2 times the maximum observed delay (3). Consider setting D to Inf for better efficiency with minimal accuracy cost for this case.