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, temporal_fa = function(sample_ages, Y, k = 2, lambda = 1e-3){ # sample_ages: Ages of samples (year BP/BCE or generations)
, Matrix of fully imputed genotypes # k: Number of factors # lambda: Hyper-parameter, p.1
, scale = FALSE)) var_Y <-apply(Y, 1, FUN = var) range_ages <-max(sample_ages) -min(sample_ages) t_n <-1 -(sample_ages -min(sample_ages))/range_ages t_n <-min(var_Y) + (max(var_Y) -min(var_Y)) * t_n #
NA, n, n) for (i in 1:n){ for ,
,
, # Eigenvectors and eigenvalues ec <-eigen(C)
, P_n <-ec$vector lambda_n <-ec$values # New factors D <-diag
, D_inv <-diag
,
,