Description Usage Arguments Details Value
For each observation (e.g. SNP), estimate the posterior probability for each association pattern.
This function calls either the t-statistic (Primo_tstat
) or
P-value (Primo_pval
) version of Primo.
Utilizes parallel computing, when available. For package documentation, see Primo-package
.
1 2 3 |
betas |
matrix of coefficient estimates. |
sds |
matrix of standard errors (for coefficient estimates). |
dfs |
vector or matrix of degrees of freedom. |
pvals |
matrix of P-values. |
alt_props |
vector of the proportions of test statistics from the 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 the tolerance threshold for convergence. |
par_size |
numeric value specifying the number of workers for parallel computing (1 for sequential processing). |
use_method |
character string denoting which method to use. Must be one of "tstat" or "pval". |
The following are additional details describing the input arguments (for m SNPs/observations measured in d studies):
alt_props
(vector of length d) must be specified whether
use_method
="tstat" or use_method
="pval".
If use_method
="tstat", the following arguments must be specified (not NULL
):
betas | m x d matrix. |
sds | m x d matrix. |
dfs | vector of length d or an m x d matrix. |
If the observations are SNPs, mafs
and N
can optionally be specified in order to adjust
error variances for minor allele frequencies.
mafs
should be either a vector of length m or an m x d matrix.
N
should be either a vector of length m or an m x d matrix.
If use_method
="pval", the following arguments must be specified (not NULL
):
pvals | m x d matrix. |
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. |
If use_method
="tstat", the list will additionally contain:
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. |
If use_method
="pval", the list will additionally contain:
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 primary 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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