format_output: Run the EM for several values of K

Description

estimateEM_several_K.OUsr uses function estimateEM on the data, for all values of K between 0 and K_max.

Usage

format_output(results_estim_EM, phylo, time = NA)

Arguments

results_estim_EM

output of function estimateEM

time

to run the function

Value

summary a data frame with K_max lines, and columns: - alpha_estim the estimated selection strength - gamma_estim the estimated root variance - beta_0_estim the estimated value of root optumum - EM_steps number of iterations needed before convergence - DV_estim has the EM diverged ? - CV_estim has the EM converged ? - log_likelihood log likelihood of the data using the estimated parameters - mahalanobis_distance_data_mean the mahalanobis distance between the data and the estimated means at the tips - least_squares the mahalanobis distance, renormalized by gamma^2: mahalanobis_distance_data_mean * gamma_estim. - mean_number_new_shifts the mean number of shifts that changed over the iterations of the EM - number_equivalent_solutions the number of equivalent solutions to the solution found. - K_try the number of shifts allowed. - complexity the complexity for K_try - time the CPU time needed.params a list of infered parametersparams_init a list of initial paramtersalpha_0 initial values of alphagmma_0 initial values of gammaZhat reconstructed node statesm_Y_estim reconstructed tip statesedge.quality for each edge, relative number of iterations in which they were present.

Details

The EM is fisrt launched for K=0, with alpha and gamma estimated. The estimated values of alpha, gamma and beta_0 found by this fisrt EM are then used as initialisation parameters for all the other runs of the EM for other K. The EMs are parralelized thanks to packages foreach and doParallel. WARNING : this code only work of OU with stationary root, on an ultrametric tree.