| phyclust.update {phyclust} | R Documentation |
This function will run the EM algorithm on initial parameters specified by users or from other initial procedures. All parameters (Eta, Mu, Q, ...) in this function will be updated.
phyclust.update(X, EMC = .EMC, ret.phyclust = NULL, K = NULL,
Eta = NULL, Mu = NULL, pi = NULL, kappa = NULL, Tt = NULL,
label = NULL, byrow = TRUE)
X |
nid/sid matrix with N rows/sequences and L columns/sites. |
EMC |
EM control. |
ret.phyclust |
an object with the class |
K |
number of clusters. |
Eta |
proportion of subpopulations, eta_k, length = |
Mu |
centers of subpopulations, dim = K*L, each row is a center. |
pi |
equilibrium probabilities, each row sums to 1. |
kappa |
transition and transversion bias. |
Tt |
total evolution time, t. |
label |
label of sequences for semi-supervised clustering. |
byrow |
advanced option for |
This function is equivalent to run exhaustEM on one specified
initial parameters, and no initial procedure is involved. While this
function is a little bit different to run phyclust with
manual.id where Mu will be reestimated as the new initials.
Simply speaking, this function only runs the EM algorithm given the
initial parameters.
All the input arguments are the same as the inputs of the functions
phyclust and phyclust.em.step.
This function returns an object with class phyclust.
Wei-Chen Chen wccsnow@gmail.com
Phylogenetic Clustering Website: http://snoweye.github.io/phyclust/
phyclust,
find.best,
phyclust.se,
phyclust.se.update.
## Not run: library(phyclust, quiet = TRUE) set.seed(1234) EMC.1 <- .EMC EMC.1$EM.iter <- 1 # the same as EMC.1 <- .EMControl(EM.iter = 1) X <- seq.data.toy$org (ret.1 <- phyclust(X, 2, EMC = EMC.1)) (ret.2 <- phyclust.update(X, ret.phyclust = ret.1)) ## End(Not run)