Estimates a residual correlation matrix from data
estimate_null_correlation( data, Ulist, init, max_iter = 30, tol = 1, est_cor = TRUE, track_fit = FALSE, prior = c("nullbiased", "uniform"), details = FALSE, ... )
| data | a mash data object, eg as created by |
|---|---|
| Ulist | a list of covariance matrices to use |
| init | the initial value for the residual correlation. If it is
not given, we use result from
|
| max_iter | maximum number of iterations to perform |
| tol | convergence tolerance |
| est_cor | whether to estimate correlation matrix (TRUE) or the covariance matrix (FALSE) |
| track_fit | add an attribute |
| prior | indicates what penalty to use on the likelihood, if any |
| details | whether to return details of the model, if it is TRUE, the mash model, the number of iterations and the value of objective functions will be returned |
| ... | other parameters pass to |
the estimated correlation matrix and the
fitted mash model
estimated residual correlation matrix
fitted mash model
Returns the estimated residual correlation matrix among conditions. We estimate the residual correlation matrix using an ad hoc em algorithm. The update in the ad hoc M step is not guaranteed to increase the likelihood, therefore, the EM algorithm is stopped before the likelihood drops. The residual correlation matrix V is estimated using the posterior second moment of the noise.
Warning: This method could take some time. The
estimate_null_correlation_simple gives a quick
approximation for the null correlation matrix.
simdata = simple_sims(100,5,1) m.1by1 = mash_1by1(mash_set_data(simdata$Bhat,simdata$Shat)) strong.subset = get_significant_results(m.1by1,0.05) random.subset = sample(1:nrow(simdata$Bhat),20) data.strong = mash_set_data(simdata$Bhat[strong.subset,], simdata$Shat[strong.subset,]) data.tmp = mash_set_data(simdata$Bhat[random.subset,], simdata$Shat[random.subset,]) U_pca = cov_pca(data.strong, 3) U_ed = cov_ed(data.strong, U_pca) Vhat = estimate_null_correlation(data.tmp, U_ed)