imImpSingle: Impute missing data for MCMC convergence checking

In idem: Inference in Randomized Controlled Trials with Death and Missingness

Impute missing data for MCMC convergence checking

Description

Call STAN model to impute missing data for an individual subject under benchmark assumption for MCMC convergence checking

Usage

imImpSingle(
  dsub,
  fit.rst,
  normal = TRUE,
  chains = 4,
  iter = 5000,
  warmup = 1000,
  control = list(adapt_delta = 0.95),
  ...,
  seed = NULL
)

Arguments

dsub	original individual subject data
fit.rst	A class IDEMFIT results generated by imFitModel.
normal	Logical variable indicating whether normality assumption should be made for the residuals
chains	STAN parameter. Number of Markov chainsm
iter	STAN parameter. Number of iterations
warmup	STAN parameter. Number of burnin.
control	STAN parameter. See rstan::stan for details.
...	other options to call STAN sampling such as thin, algorithm. See rstan::sampling for details.
seed	Random seed

Value

NULL if there is no missing data in dsub

Otherwise, return a class IDEMSINGLE object that contains a list with components

dsub

original data of the subject

rst.stan

A stan.fit class result returned from rstan::sampling

complete

A dataframe with complete data for the selected subject

Examples

im.abc <- imData(abc, trt="TRT", surv="SURV", outcome=c("Y1","Y2"),
                 y0=NULL, endfml="Y2",
                 trt.label = c("UC+SBT", "SAT+SBT"),
                 cov=c("AGE"), duration=365, bounds=c(0,100));
im.fit <- imFitModel(im.abc);
im.imp <- imImpSingle(abc[1,], im.fit, chains = 4, iter = 200, warmup = 100);

idem documentation built on Aug. 9, 2023, 5:08 p.m.