Description Usage Arguments Value
For each SNP, estimates the posterior probability for each association pattern.
Uses parameters estimated by a previous run of Primo_pval
or Primo_chiMix
to estimate probabilities for SNPs missing in one or more studies.
P-values from non-missing studies are used as input.
Utilizes parallel computing, when available.
1 2 | Primo_missdata_pval(pvals, trait_idx, pis, Gamma, A, df_alt,
par_size = 1)
|
pvals |
matrix of P-values from test statistics. |
trait_idx |
integer vector of the columns corresponding to non-missing phenotypes/studies. |
pis |
vector of the estimated proportion of observations belonging to each association pattern |
Gamma |
correlation matrix. |
A |
vector of scaling factors under the alternative distributions. |
df_alt |
vector of degrees of freedom approximated for the alternative distributions. |
par_size |
numeric value; specifies the number of workers for parallel computing (1 for sequential processing). |
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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