fit_lc: Fit a latent class model

In haziqj/diagacc: Modelling Diagnostic Accuracy using Latent Class Models

Description

Fit a latent class model using MCMC.

Usage

fit_lc(
  X,
  n.sample = 2000,
  n.chains = 2,
  n.thin = 1,
  n.burnin = 800,
  n.adapt = 200,
  raw = FALSE,
  runjags.method = "rjags",
  silent = FALSE,
  calcSE = TRUE,
  gold.std = FALSE,
  method = c("MCMC", "EM")
)

Arguments

X	(Matrix) Item responses data set.
n.sample	Number of MCMC samples.
n.chains	Number of chains.
n.thin	Thinning value.
n.burnin	Number of burn-in.
n.adapt	Number of adaptation samples.
raw	(Logical) Return the randomLCA or runjags object?
runjags.method	Parallel or normal method. See runjags documentation.
silent	(Logical) Suppress output.
calcSE	(logical) Calculate standard error of estimates for randomLCA fit.
gold.std	(Logical) Is the last item/column in X the gold standard?
method	(DEPRECATED–Use MCMC only) EM algorithm or MCMC.

Details

Uninformative priors are used, e.g. Unif(0, 1) for probabilities. Initial value for the prevalence is set at 0.1, the disease indicators to zero for all units, and the sensitivities and specificities to 0.9.

Note that when gold.std is TRUE, then the last column in X is assumed to be the gold standard item responses. Thus, the sensitivities and specificities attached to this item is fixed to 1.

haziqj/diagacc documentation built on Nov. 21, 2020, 5:40 p.m.