# Multichannel distances between sequences

### Description

Compute multichannel pairwise distances between sequences. Several metrics are available: optimal matching (OM), the longest common subsequence (LCS), the Hamming distance (HAM) and the Dynamic Hamming Distance (DHD).

### Usage

1 2 3 |

### Arguments

`channels` |
A list of state sequence objects defined
with the |

`method` |
a character string indicating the metric to be used.
One of |

`norm` |
if |

`indel` |
A vector with an insertion/deletion cost for each channel (OM method). |

`sm` |
A list with a substitution-cost matrix for each channel (OM, HAM and DHD
method) or a list of method names for generating the substitution-costs
(see |

`with.missing` |
Must be set to |

`full.matrix` |
If |

`link` |
One of |

`cval` |
Substitution cost for |

`miss.cost` |
Missing values substitution cost, see |

`cweight` |
A vector of channel weights. Default is 1 (same weight for each channel). |

### Details

The `seqdistmc`

function returns a matrix of multichannel distances between sequences. The available metrics (see 'method' option) are optimal matching (`"OM"`

), longest common subsequence (`"LCS"`

), Hamming distance (`"HAM"`

) and Dynamic Hamming Distance (`"DHD"`

). See `seqdist`

for more information about distances between sequences. The `seqdistmc`

function computes a multichannel distance in two steps following the strategy proposed by Pollock (2007). First it builds a new sequence object derived from the combination of the sequences of each channel. Second, it derives the substitution cost matrix by summing (or averaging) the costs of substitution across channels. It then calls `seqdist`

to compute the final matrix. Normalization may be useful when dealing with sequences that are not all of the same length. For details on the applied normalization, see `seqdist`

.

### Value

A matrix of pairwise distances between sequences is returned.

### Author(s)

Matthias Studer (with Gilbert Ritschard for the help page)

### References

Pollock, Gary (2007) Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis. *Journal of the Royal Statistical Society: Series A* **170**, Part 1, 167–183.

### See Also

`seqsubm`

, `seqdef`

, `seqdist`

.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
data(biofam)
## Building one channel per type of event left, children or married
bf <- as.matrix(biofam[, 10:25])
children <- bf==4 | bf==5 | bf==6
married <- bf == 2 | bf== 3 | bf==6
left <- bf==1 | bf==3 | bf==5 | bf==6
## Building sequence objects
child.seq <- seqdef(children)
marr.seq <- seqdef(married)
left.seq <- seqdef(left)
## Using transition rates to compute substitution costs on each channel
mcdist <- seqdistmc(channels=list(child.seq, marr.seq, left.seq),
method="OM", sm =list("TRATE", "TRATE", "TRATE"))
## Using a weight of 2 for children channel and specifying substitution-cost
smatrix <- list()
smatrix[[1]] <- seqsubm(child.seq, method="CONSTANT")
smatrix[[2]] <- seqsubm(marr.seq, method="CONSTANT")
smatrix[[3]] <- seqsubm(left.seq, method="TRATE")
mcdist2 <- seqdistmc(channels=list(child.seq, marr.seq, left.seq),
method="OM", sm =smatrix, cweight=c(2,1,1))
``` |