# hwe.ibf.mc: Testing Hardy-Weinberg Equilibrium Using an Intrinsic Prior... In HWEintrinsic: Objective Bayesian Testing for the Hardy-Weinberg Equilibrium Problem

 hwe.ibf.mc R Documentation

## Testing Hardy-Weinberg Equilibrium Using an Intrinsic Prior Approach

### Description

This function implements the Monte Carlo estimation of the Bayes factor based on intrinsic priors for the Hardy-Weinberg testing problem as described in Consonni et al. (2011).

### Usage

``````hwe.ibf.mc(y, t, M = 10000, verbose = TRUE)
``````

### Arguments

 `y` an object of class "HWEdata". `t` training sample size. `M` number of Monte Carlo iterations. `verbose` logical; if TRUE the function prints the detailed calculation progress.

### Details

This function implements a Monte Carlo approximation using importance sampling of the Bayes factor based on intrinsic priors.

### Value

`hwe.ibf.mc` returns an object of the class "HWEintr".

### Note

The Bayes factor computed here is for the unrestricted model (`M_1`) against the Hardy-Weinberg case (`M_0`).

### Author(s)

Sergio Venturini sergio.venturini@unicatt.it

### References

Consonni, G., Moreno, E., and Venturini, S. (2011). "Testing Hardy-Weinberg equilibrium: an objective Bayesian analysis". Statistics in Medicine, 30, 62–74. https://onlinelibrary.wiley.com/doi/10.1002/sim.4084/abstract

`hwe.ibf`, `hwe.ibf.plot`.

### Examples

``````# Example 1 #
## Not run:
# ATTENTION: the following code may take a long time to run! #

data(GuoThompson9)
plot(GuoThompson9)
n <- sum(GuoThompson9@data.vec, na.rm = TRUE)
out <- hwe.ibf.mc(GuoThompson9, t = n/2, M = 100000, verbose = TRUE)
summary(out, plot = TRUE)

## End(Not run)

# Example 2 #
## Not run:
# ATTENTION: the following code may take a long time to run! #

M <- 300000
f <- seq(.1, 1, .05)
n <- sum(GuoThompson9@data.vec, na.rm = TRUE)
out <- hwe.ibf.plot(y = GuoThompson9, t.vec = round(f*n), M = M)

## End(Not run)
``````

HWEintrinsic documentation built on Sept. 8, 2023, 5:56 p.m.