Primo: Estimate posterior probabilities of association patterns.
In kjgleason/Primo: Package in R for Integrative Multi-Omics association analysis

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.

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Primo(betas = NULL, sds = NULL, dfs = NULL, pvals = NULL,
  alt_props, mafs = NULL, N = NULL, Gamma = NULL, tol = 0.001,
  par_size = 1, use_method = "tstat")

`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.

kjgleason/Primo documentation built on Sept. 7, 2021, 3:58 a.m.