nof1.run: Run the model using the nof1 object for individual analysis

In jiabei-yang/nof1ins: N-of-1 study general analysis tool

Description

This is the core function that runs the model in our program. Before running this function, we need to specify data, prior, JAGS code, etc. using nof1.data.

Usage

nof1.run(
  nof1,
  inits = NULL,
  n.chains = 3,
  max.run = 1e+05,
  setsize = 10000,
  n.run = 50000,
  conv.limit = 1.05,
  extra.pars.save = NULL
)

Arguments

nof1	nof1 object created from nof1.data function
inits	Initial values for the parameters being sampled. If left unspecified, program will generate reasonable initial values.
n.chains	Number of chains to run
max.run	Maximum number of iterations that user is willing to run. If the algorithm is not converging, it will run up to max.run iterations before printing a message that it did not converge
setsize	Number of iterations that are run between convergence checks. If the algorithm converges fast, user wouldn't need a big setsize. The number that is printed between each convergence checks is the gelman-rubin diagnostics and we would want that to be below the conv.limit the user specifies.
n.run	Final number of iterations that the user wants to store. If after the algorithm converges, user wants less number of iterations, we thin the sequence. If the user wants more iterations, we run extra iterations to reach the specified number of runs
conv.limit	Convergence limit for Gelman and Rubin's convergence diagnostic.
extra.pars.save	Parameters that user wants to save besides the default parameters saved. See code using cat(nof1$code) to see which parameters can be saved.

Value

nof1	nof1 object
inits	Initial values that are either specified by the user or generated as a default
pars.save	Parameters that are saved. Add more parameters in extra.pars.save if other variables are desired
data_rjags	Data that is put into rjags function jags.model
burnin	Half of the converged sequence is thrown out as a burnin
n.thin	If the number of iterations user wants (n.run) is less than the number of converged sequence after burnin, we thin the sequence and store the thinning interval
samples	MCMC samples stored using jags. The returned samples have the form of mcmc.list and can be directly applied to coda functions
max.gelman	Maximum Gelman and Rubin's convergence diagnostic calculated for the final sample

Examples

laughter
Y <- laughter$Y
Treat <- laughter$Treat
nof1 <- nof1.data(Y, Treat, ncat = 11, baseline = "Usual Routine", response = "ordinal")
result <- nof1.run(nof1)
summary(result$samples)

jiabei-yang/nof1ins documentation built on Sept. 7, 2021, 1:10 p.m.