WH {DCLEAR}

WH

Description

implementation of weighted hamming algorithm

Usage

WH(x, InfoW, dropout = FALSE)

Arguments

x

Sequence object of 'phyDat' type.

InfoW

Weight vector for the calculation of weighted hamming distance

dropout

Different weighting strategy is taken to consider interval dropout with dropout = 'TRUE'. Default is, dropout = 'FALSE'.

Value

Calculated distance matrix of input sequences. The result is a 'dist' class object.

Author(s)

Il-Youp Kwak

Examples

set.seed(1)
library(phangorn)
mu_d1 = c( 30, 20, 10, 5, 5, 1, 0.01, 0.001)
mu_d1 = mu_d1/sum(mu_d1)
simn = 10 # number of cell samples
m = 10  ## number of targets
sD = sim_seqdata(sim_n = simn, m = m, mu_d = 0.03,
        d = 12, n_s = length(mu_d1), outcome_prob = mu_d1, p_d = 0.005 )

## RF score with hamming distance
D_h = dist.hamming(sD$seqs)
tree_h= NJ(D_h)
RF.dist(tree_h, sD$tree, normalize = TRUE)

## RF score with weighted hamming
InfoW = -log(mu_d1)
InfoW[1:2] = 1
InfoW[3:7] = 4.5

D_wh = WH(sD$seqs, InfoW)
tree_wh= NJ(D_wh)
RF.dist(tree_wh, sD$tree, normalize = TRUE)

## RF score with weighted hamming, cosidering dropout situation
nfoW = -log(mu_d1)
InfoW[1] = 1
InfoW[2] = 12
InfoW[3:7] = 3

D_wh2 = WH(sD$seqs, InfoW, dropout=TRUE)
tree_wh2= NJ(D_wh2)
RF.dist(tree_wh2, sD$tree, normalize = TRUE)