correctedOR: Model without misclassification

In formidify/BayesSenMC: Different models of posterior distributions of adjusted odds ratio

Description

Generate a stanfit object corresponding to a posterior distribution of uncorrected odds ratio given no misclassification.

Usage

correctedOR(a, N1, c, N0, name = "Corrected Model", chains = 2,
  traceplot = FALSE, inc_warmup = FALSE, window = NULL,
  refresh = 0, seed = NA, ...)

Arguments

a	# of exposed subjects in the case group.
N1	# of total subjects in the case group.
c	# of exposed subjects in the control group.
N0	# of total subjects in the control group.
name	a string of the name of the model. Default to "Corrected Model".
chains	number of Markov Chains. Default to 2.
traceplot	Logical, defaulting to FALSE. If TRUE it will draw the traceplot corresponding to one or more Markov chains.
inc_warmup	Only evaluated when traceplot = TRUE. TRUE or FALSE, indicating whether or not to include the warmup sample in the traceplot; defaults to FALSE.
window	Only evaluated when traceplot = TRUE. A vector of length 2. Iterations between window[1] and window[2] will be shown in the plot. The default shows all iterations if inc_warmup is TRUE and all iterations from the sampling period only if inc_warmup is FALSE. If inc_warmup is FALSE the iterations specified in window do not include iterations from the warmup period. The default number of iterations is 2000 unless otherwise specified in the optional iter argument.
refresh	an integer value used to control how often the progress of sampling is reported. By default, the progress indicator is turned off, thus refresh <= 0. If on, refresh = max(iter/10, 1) is generally recommended.
seed	the seed for random number generation. See stan for more details.
...	optional parameters passed to stan.

Value

It returns a stanfit object of this model, which inherits stanfit class methods. See here for more details.

Examples

# Case-control study data of Bipolar Disorder with rheumatoid arthritis (Farhi et al. 2016)
# Data from \url{https://www.sciencedirect.com/science/article/pii/S0165032715303864#bib13}

# 3 MCMC chains with 10000 iterations each
## Not run: 
correctedOR(a = 66, N1 = 11782, c = 243, N0 = 57973, chains = 3, iter = 10000, seed = 0)

## End(Not run)
correctedOR(a = 66, N1 = 11782, c = 243, N0 = 57973, traceplot = TRUE)

formidify/BayesSenMC documentation built on May 4, 2019, 6:36 p.m.