Description Usage Arguments Value
For each observation (e.g. SNP), estimates the posterior probability for each association pattern.
Uses parameters estimated by a previous run of Primo_tstat
or Primo_modT
to estimate probabilities for SNPs missing in one or more studies.
Utilizes parallel computing, when available.
1 2 | Primo_missdata_tstat(betas, sds, dfs, trait_idx, mafs = NULL, N = NULL,
pis, Gamma, prior_df, prior_var, unscaled_var, par_size = 1)
|
betas |
matrix of coefficient estimates. |
sds |
matrix of standard errors (for coefficient estimates). |
dfs |
vector or matrix of degrees of freedom. |
trait_idx |
integer vector of the columns corresponding to non-missing phenotypes/studies. |
mafs |
vector or matrix of minor allele frequencies (MAFs).
If |
N |
vector or matrix of number of subjects.
Should be specified if |
pis |
vector of the estimated proportion of observations belonging to each association pattern. |
Gamma |
correlation matrix. |
prior_df |
vector of the prior degrees of freedom for each marginal distribution. |
prior_var |
vector of the prior variance estimators for each marginal distribution. |
unscaled_var |
vector of the unscaled variance priors on non-zero coefficients for each marginal distribution. |
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. |
Tstat_mod | matrix of moderated t-statistics. |
V_mat | matrix of scaling factors under the alternative distribution. |
mdf_sd_mat | matrix of standard deviation adjustment according to
moderated degrees of freedom:sqrt(df/(df-2)) . |
prior_df | vector of the prior degrees of freedom for each marginal distribution. |
prior_var | vector of the prior variance estimators for each marginal distribution. |
unscaled_var | vector of the unscaled variance priors on non-zero coefficients for each marginal distribution. |
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.