Description Usage Arguments Details Value
For each observation (e.g. SNP), estimates the posterior probability of each association pattern. Utilizes parallel computing, when available.
1 | Primo_pval(pvals, alt_props, Gamma = NULL, tol = 0.001, par_size = 1)
|
pvals |
matrix of P-values from test statistics. |
alt_props |
vector of the proportions of test statistics from alternative densities. |
Gamma |
correlation matrix. |
tol |
numeric value specifying the tolerance threshold for convergence. |
par_size |
numeric value specifying the number of workers for parallel computing (1 for sequential processing). |
The following are additional details describing the input arguments (for m SNPs/observations measured in d studies):
pvals | m x d matrix. |
alt_props | vector of length d. |
Gamma | d x d matrix. |
If NULL , will be estimated using observations where all p < 5.7e-7. |
|
A list with the following elements:
post_prob | matrix of posterior probabilities (each column corresponds to an association pattern). |
pis | vector of estimated proportion of observations belonging to each association pattern. |
D_mat | matrix of densities under each association pattern. |
Gamma | correlation matrix. |
chi_mix | matrix of -2log(P)-values. |
A | vector of scaling factors under the alternative distributions. |
df_alt | vector of degrees of freedom approximated for the alternative distributions. |
The main element of interest for inference is the posterior probabilities matrix, post_prob
.
The estimated proportion of observations belonging to each association pattern, pis
, may
also be of interest. The remaining elements are returned primarily for use by other functions.
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