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 2 | Primo_tstat(betas, sds, dfs, alt_props, mafs = NULL, N = NULL,
Gamma = NULL, tol = 0.001, par_size = 1)
|
betas |
matrix of coefficient estimates. |
sds |
matrix of standard errors (for coefficient estimates). |
dfs |
vector or matrix of degrees of freedom. |
alt_props |
vector of the proportions of test statistics from alternative densities. |
mafs |
vector or matrix of minor allele frequencies (MAFs). |
N |
vector or matrix of number of subjects. |
Gamma |
correlation matrix. |
tol |
numeric value specifying 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):
betas | m x d matrix. |
sds | m x d matrix. |
dfs | vector of length d or an m x d matrix. |
alt_props | vector of length d. |
mafs | vector of length m or an m x d matrix. |
If NULL , error variances will not be adjusted for MAF. |
|
N | vector of length d or an m x d matrix. |
Must be specified if mafs!=NULL . |
|
Gamma | d x d matrix. |
If NULL , will be estimated using observations where all |t| < 5. |
|
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.