phyclust {phyclust} | R Documentation

## The Main Function of phyclust

### Description

The main function of phyclust implements finite mixture models for sequence data that the mutation processes are modeled by evolution processes based on Continuous Time Markov Chain theory.

### Usage

    phyclust(X, K, EMC = .EMC, manual.id = NULL, label = NULL, byrow = TRUE)

### Arguments

`X` | nid/sid matrix with N rows/sequences and L columns/sites.
`K` | number of clusters.
`EMC` | EM control.
`manual.id` | manually input class ids.
`label` | label of sequences for semi-supervised clustering
`byrow` | advanced option for `X`, default = TRUE.

### Details

`X` should be a numerical matrix containing sequence data that can be transfered by `code2nid` or `code2sid`.

`EMC` contains all options used for EM algorithms.

`manual.id` manually input class ids as an initialization only for the initialization method, 'manualMu'.

`label` indicates the known clusters for labeled sequences which is a vector with length `N` and has values from `0` to `K`. `0` indicates clusters are unknown. `label = NULL` is for unsupervised clustering. Only un- and semi-supervised clustering are implemented.

`byrow` used in bootstraps to avoid transposing matrix 'X'. If FALSE, then the 'X' should be have the dimension L*K.

### Value

A list with class `phyclust` will be returned containing several elements as the following:

`'N.X.org'` | number of sequences in the `X` matrix.
`'N.X.unique'` | number of unique sequences in the `X` matrix.
`'L'` | number of sites, length of sequences, number of column of the `X` matrix.
`'K'` | number of clusters.
`'Eta'` | proportion of subpopulations, eta_k, length = `K`, sum to 1.
`'Z.normalized'` | posterior probabilities, Z_nk, each row sums to 1.
`'Mu'` | centers of subpopulations, dim = K*L, each row is a center.

`'QA'`

Q matrix array, information for the evolution model, a list contains:

'pi' | equilibrium probabilities, each row sums to 1.
'kappa' | transition and transversion bias.
'Tt' | total evolution time, t.
'identifier' | identifier for QA.

`'logL'` | log likelihood values.
`'p'` | number of parameters.
`'bic'` | BIC, -2logL + plogN.
`'aic'` | AIC, -2logL + 2p.
`'N.seq.site'` | number of segregating sites.
`'class.id'` | class id for each sequences based on the maximum posterior.
`'n.class'` | number of sequences in each cluster.

`'conv'`

convergence information, a list contains:

'eps' | relative error.
'error' | error if the likelihood decreased.
'flag' | convergence state.
'iter' | convergence iterations.
'inner.iter' | convergence of inner iterations other than EM.
'cm.iter' | convergence of CM iterations.
'check.param' | parameter states.

`'init.procedure'` | initialization procedure.
`'init.method'` | initialization method.
`'substitution.model'` | substitution model.
`'edist.model'` | evolution distance model.
`'code.type'` | code type.
`'em.method'` | EM algorithm.
`'boundary.method'` | boundary method.
`'label.method'` | label method.

### ToDo(s)

- make a general class for `Q` and `QA`.

### Author(s)

Wei-Chen Chen wccsnow@gmail.com

### References

Phylogenetic Clustering Website: https://snoweye.github.io/phyclust/

### See Also

`.EMC`, `.EMControl`, `find.best`, `phyclust.se`. `phyclust.se.update`.

### Examples

    library(phyclust, quiet = TRUE)

    X <- seq.data.toy$org

    set.seed(1234)
    (ret.1 <- phyclust(X, 3))

    EMC.2 <- .EMC
    EMC.2$substitution.model <- "HKY85"
    # the same as EMC.2 <- .EMControl(substitution.model = "HKY85")

    (ret.2 <- phyclust(X, 3, EMC = EMC.2))

    # for semi-supervised clustering
    semi.label <- rep(0, nrow(X))
    semi.label[1:3] <- 1
    (ret.3 <- phyclust(X, 3, EMC = EMC.2, label = semi.label))

[Package phyclust version 0.1-25 Index]