Description Usage Arguments Details Value Note Examples
View source: R/sampler_functions.R
This function runs Monte Carlo simulations for each variant's conditional mean priors.
1 | summarise_cond_prior(cond_prior, n_cores = 1, n_samp = 50000)
|
cond_prior |
a conditional prior object |
n_cores |
number of cores for parallelization |
n_samp |
number of prior simulation draws |
This is used to ensure that the prior is not TOO specific. While we
want the prior distribution to accurately reflect prior beliefs on a given
variant's effects, it would be undesirable for the prior to pre-specify
that the variant is functional. If the prior_is_func
column in the
output of this function is all FALSE, this means that no variant is a
priori functional.
If one does get a conditional prior that has variants that are a
priori functional, this can be addressed by increasing the
min_neighbors
argument in fit_cond_prior
. See the
documentation of that function for details.
Currently this function only simulates the mean parameters as in practice the dispersion parameters don't vary systematically between alleles and/or variants.
a data frame of RNA priors with prior simulation summary statistics
simulations for individual variants can be obtained with
summarise_one_prior()
and sample_from_prior()
1 | summarise_cond_prior(cond_prior_example, n_samp = 1000, n_cores = 1)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.