impute {oncomsm}

Sample visits from predictive distribution

Description

impute() samples visits for individuals in data and potentially missing individuals up to a maximum of n_per_group from the posterior predictive distribution of the given model.

sample_predictive() draws samples from the predictive distribution of a model given a parameter sample.

Usage

impute(
  model,
  data,
  nsim,
  n_per_group = NULL,
  sample = NULL,
  p = NULL,
  shape = NULL,
  scale = NULL,
  now = NULL,
  seed = NULL,
  nsim_parameters = 1000L,
  warmup_parameters = 250L,
  nuts_control = list(),
  as_mstate = FALSE,
  ...
)

sample_predictive(
  model,
  nsim,
  n_per_group,
  sample = NULL,
  p = NULL,
  shape = NULL,
  scale = NULL,
  seed = NULL,
  nsim_parameters = 1000L,
  warmup_parameters = 250,
  nuts_control = list(),
  as_mstate = FALSE,
  ...
)

Arguments

model

an object of class srpmodel containing prior information

data

a data frame with variables ⁠subject_id<chr>⁠ (subject identifier), ⁠group_id<chr>⁠ (group identifier), ⁠t<dbl>⁠ (time of visit, relative to first visit in study), ⁠state<chr>⁠ (state recorded at visit). Allowed states are "stable", "response", "progression" (or death), and "EOF" (end of follow-up). The EOF state marks the end of an individual's follow-up before the absorbing state "progression".

nsim

integer, number of samples to draw

n_per_group

integer vector with number of individuals per group.

sample

a stanfit object with samples from the respective model.

p

numeric, vector of optional fixed response probabilities to use for sampling

shape

numeric, matrix of optional fixed Weibull shape parameters to use for sampling must be a matrix of dim c(n_groups, 3) where the second dimension corresponds to the transitions between s->r, s->p, r->p

scale

numeric, matrix of optional fixed Weibull scale parameters to use for sampling must be a matrix of dim c(n_groups, 3) where the second dimension corresponds to the transitions between s->r, s->p, r->p

now

numeric, time since first visit in data if not last recorded visit time

seed

integer, fixed random seed; NULL for no fixed seed

nsim_parameters

integer, number of parameter samples

warmup_parameters

integer, number of warmup samples for the rstan sampler before retaining samples of the parameters.

nuts_control

list, parameters for NUTS algorithm see control argument inrstan::stan()

as_mstate

logical, return data in mstate format?

...

further arguments passed to method implementations

Value

a data frame with variables ⁠subject_id<chr>⁠ (subject identifier), ⁠group_id<chr>⁠ (group identifier), ⁠t<dbl>⁠ (time of visit, relative to first visit in study), ⁠state<chr>⁠ (state recorded at visit) ⁠iter<int>⁠ (re-sample indicator). Allowed states are "stable", "response", "progression" (or death), and "EOF" (end of follow-up). The EOF state marks the end of an individual's follow-up before the absorbing state "progression".

See Also

sample_prior() sample_posterior()

Examples

mdl <- create_srpmodel(A = define_srp_prior())
tbl <- tibble::tibble(
  subject_id = c("A1", "A1"),
  group_id = c("A", "A"),
  t = c(0, 1.5),
  state = c("stable", "stable")
)
impute(mdl, tbl, 1L, seed = 38L)

sample_predictive(mdl, 1L, 20L, seed = 38L)