Multivariate Adaptive Shrinkage

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Documentation for package ‘mashr’ version 0.2.79

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

contrast_matrix	Create contrast matrix
cov_canonical	Compute a list of canonical covariance matrices
cov_ed	Perform "extreme deconvolution" (Bovy et al) on a subset of the data
cov_flash	Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices
cov_pca	Perform PCA on data and return list of candidate covariance matrices
cov_udi	Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models
estimate_null_correlation_simple	Estimate null correlations (simple)
extreme_deconvolution	Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data
get_estimated_pi	Return the estimated mixture proportions
get_log10bf	Return the Bayes Factor for each effect
get_n_significant_conditions	Count number of conditions each effect is significant in
get_pairwise_sharing	Compute the proportion of (significant) signals shared by magnitude in each pair of conditions, based on the poterior mean
get_pairwise_sharing_from_samples	Compute the proportion of (significant) signals shared by magnitude in each pair of conditions
get_samples	Return samples from a mash object
get_significant_results	Find effects that are significant in at least one condition
mash	Apply mash method to data
mash_1by1	Perform condition-by-condition analyses
mash_compute_loglik	Compute loglikelihood for fitted mash object on new data.
mash_compute_posterior_matrices	Compute posterior matrices for fitted mash object on new data
mash_compute_vloglik	Compute vector of loglikelihood for fitted mash object on new data
mash_estimate_corr_em	Fit mash model and estimate residual correlations using EM algorithm
mash_plot_meta	Plot metaplot for an effect based on posterior from mash
mash_set_data	Create a data object for mash analysis.
mash_update_data	Update the data object for mash analysis.
simple_sims	Create some simple simulated data for testing purposes
simple_sims2	Create some more simple simulated data for testing purposes
sim_contrast1	Create simplest simulation, cj = mu 1 data used for contrast analysis
sim_contrast2	Create simulation with signal data used for contrast analysis.