compute_E.simple | R Documentation |
compute_E.simple
computes the E step in the simple case where the invert matrix Sigma_YY_inv is given
compute_E.simple( phylo, times_shared, distances_phylo, process, params_old, masque_data = c(rep(TRUE, attr(params_old, "p_dim") * length(phylo$tip.label)), rep(FALSE, attr(params_old, "p_dim") * phylo$Nnode)), F_moments, Y_data_vec_known, miss = rep(FALSE, attr(params_old, "p_dim") * length(phylo$tip.label)), Y_data, U_tree, ... )
phylo |
Input tree. |
Y_data |
: vector indicating the data at the tips |
sim |
(list) : result of function |
Sigma |
: variance-covariance matrix, result of function |
Sigma_YY_inv |
: invert of the variance-covariance matrix of the data |
This function takes parameters sim, Sigma and Sigma_YY_inv from
compute_mean_variance.simple
. It uses functions
extract.variance_covariance
, extract.covariance_parents
, and
extract_simulate_internal
to extract the needed quantities from these objects.
conditional_law_X (list) : list of conditional statistics : "expectation" : matrix of size p x (ntaxa+Nnode), with ntaxa first columns set to Y_data (tips), and from ntaxa+1 to conditional expectation of the nodes conditioned to the tips E[Z_j|Y] "variances" : array of size p x p x (ntaxa+Nnode) with ntaxa first matrices of zeros (tips) and conditional variance of the nodes conditioned to the tips Var[Z_j|Y] "covariances" : array of size p x p x (ntaxa+Nnode) with ntaxa first matrices of zeros (tips) and conditional covariance of the nodes and their parents conditioned to the tips Cov[Z_j,Z_pa(j)|Y], with NA for the root. "optimal.values" : matrix of size p x ntaxa+Nnode of optimal values beta(t_j)
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