hwe.ibf.mc | R Documentation |
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).
hwe.ibf.mc(y, t, M = 10000, verbose = TRUE)
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. |
This function implements a Monte Carlo approximation using importance sampling of the Bayes factor based on intrinsic priors.
hwe.ibf.mc
returns an object of the class "HWEintr".
The Bayes factor computed here is for the unrestricted model (M_1
) against the Hardy-Weinberg case (M_0
).
Sergio Venturini sergio.venturini@unicatt.it
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
.
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.