SingleTable.modelFit: Exact posterior inference based on a single 2x2 table

Description

This function conducts exact posterior inference based on the object created by SingleTable.create.

Usage

SingleTable.modelFit(
  single_table_Obj,
  method = "exact",
  verbose = TRUE,
  control = list()
)

Value

An object of singletable class is returned including the following non-null values:

measure: the value of measure argument.
model: the value of model argument.
data: a numeric vector of input data with components: y1, n1, y2, n2.
parameter: a numeric vector of the hyperparameters: a1, b1, a2, b2, and rho.
method: the value of method argument.
sample: a list of samples for the posterior and prior distributions.
density: a list of the density of the posterior and prior distributions.

Arguments

single_table_Obj: The object created by SingleTable.create.
method: a character string specifying the method. Options are exact and sampling. exact (default) is a method based on Monte Carlo sampling. exact is exact method.
verbose: a logical value; if TRUE(default), the detailed summary messages are displayed, else the messages are omitted.
control: a list can be specified to control the fitting process. Options are stated in details.

Details

control list can be specified to control the fitting process:

n_samples: number of posterior samples; Defualt is 5000.
mcmc_initial: initial values for (p1, p2) in MCMC; Default is c(0.5, 0.5).
upper_bound: upper bound for the measure. Default is 100.
lower_bound: lower bound for the measure. For RD, default is -1. For RR/OR, defualt is 0.
num_grids: number of grids to calculate density; The defualt is 20498.

Examples

Run this code

## Assume we have a 2x2 table:{{40,56},{49,60}} and set prior parameters as a1=b1=a2=b2=rho=0.5.
 # \donttest{
 library(mmeta)
 library(ggplot2)
 # ########################## If sampling method is used ############################
 ## Create object \code{single_table_obj_samling}
 single_table_obj_samling <- SingleTable.create(a1=0.5,b1=0.5,
 a2=0.5,b2=0.5,rho=0.5, y1=40, n1=96, y2=49, n2=109,model="Sarmanov",measure="OR")
 ## model fit
 single_table_obj_samling <- SingleTable.modelFit(single_table_obj_samling,
 method = 'sampling')
 ## Control list option examples
 ## set number of posterior samples as 3000 (default is 5000)
 single_table_obj_samling <- SingleTable.modelFit(single_table_obj_samling,
 method = 'sampling', control = list(n_sample = 3000))
 ## set initial values for MCMC is c(0.2, 0,4) (default is c(0.5,0.5))
 single_table_obj_samling <- SingleTable.modelFit(single_table_obj_samling,
 method = 'sampling', control = list(mcmc_initial = c(0.2,0.4)))
 ## set upper bound for the measure is 20( default is 100)
 single_table_obj_samling <- SingleTable.modelFit(single_table_obj_samling,
 method = 'sampling', control = list(upper_bound = 20))
 # ########################### If exact method is used ##############################
 ## Create object \code{single_table_obj_exact}
 single_table_obj_exact <- SingleTable.create(a1=0.5, b1=0.5, a2=0.5, b2=0.5,
 rho=0.5, y1=40, n1=96, y2=49, n2=109, model="Sarmanov",measure="OR")
 ## model fit
 single_table_obj_exact <- SingleTable.modelFit(single_table_obj_exact, method = 'exact')
 ## The options of \code{control} list specifying the fitting process are similar
 ## to the codes shown above.
 # }