Description Usage Arguments Details Value Note Examples
View source: R/annotation_analysis.R
This function evaluates prior ratios of MLE estimates under user-supplied conditional and marginal prior distributions. If this ratio is above one (or equivalently if the log is above 0), this implies that the conditional prior improves upon the marginal prior for that particular parameter.
1 2 3 4 5 6 7 | get_prior_ratios(
mpra_data,
marg_prior,
cond_prior,
n_cores = 1,
verbose = TRUE
)
|
mpra_data |
a data frame of MPRA data |
marg_prior |
a marginal prior |
cond_prior |
a conditional prior |
n_cores |
number of cores to use in parallel |
verbose |
logical indicating whether to print messages |
the inputs marg_prior
and cond_prior
can be taken
directly as the outputs of fit_marg_prior and fit_cond_prior.
This output is returned as the difference of log densities.
a data frame of MLE mean and dispersion parameters by variant_id, sample_id, and barcode giving prior ratio for each
The priors for DNA counts are always the same (since we have no prior knowledge of DNA counts)
The current maximum likelihood estimates are sub-par and will be improved. Interpret with caution.
1 |
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