TraMineR (version 1.1)

seqdist: Distances between sequences

Description

Compute distances between sequences. Several metrics are available: optimal matching and other metrics proposed by Elzinga (2008).

Usage

seqdist(seqdata, method, refseq=NULL, norm=FALSE, 
	indel=1, sm, with.miss = FALSE, full.matrix = TRUE)

Arguments

seqdata

a sequence object as defined by the the seqdef function.

method

a character string indicating the metric to use for distance. One of "OM" (optimal matching),"LCP" (Longest Common Prefix), "LCS" (Longest Common Subsequence).

refseq

Optional reference sequence to compute the distances from. Can be the index of a sequence in the data set or 0 for the most frequent sequence in the data set. If refseq is specified, a vector with distances between the sequences in the data set and the re

norm

if TRUE, OM, LCP and LCS distances are rescaled to be unit free, ie insensitive to sequences length. Default to FALSE.

indel

the insertion/delation cost if optimal matching ("OM") is choosed. Default to 1. Don't specify if other metric is used.

sm

substitution-cost matrix for the optimal matching method ("OM"). Default to NA. Don't specify if other method is used.

with.miss

If sequences contain gaps (missing values) (see seqdef for the available options for handling missing values), this option must be set to TRUE to compute distances, otherwise the function will stop. If optim

full.matrix

If TRUE (default), the full distance matrix is returned. This is for compatibility with the previous version of the seqdist function. If false, an object of class dist is returned, that is, a vector containing

Value

• a distance matrix or a vector containing distances to the specified reference sequence.

encoding

latin1

Details

The seqdist function returns a matrix of distances between sequences or a vector of distances to a reference sequence. The available metrics (see 'method' option) are optimal matching ("OM"), longuest common prefix ("LCP") or longuest common subsequence ("LCS"). Distances can optionaly be normalized (see 'norm' option). For more details, see Elzinga (2008) and Gabadinho (2008).

References

Elzinga, Cees H. (2008). Sequence analysis: Metric representations of categorical time series. Sociological Methods and Research, forthcoming. Gabadinho, A., G. Ritschard, M. Studer and N. S. M�ller (2008). Mining Sequence Data in R with TraMineR: A user's guide. Department of Econometrics and Laboratory of Demography, University of Geneva.

See Also

seqsubm.

Examples

## optimal matching distances with substitution cost matrix 
## using transition rates
data(biofam)
biofam.seq <- seqdef(biofam, 10:25)
costs <- seqsubm(biofam.seq, method="TRATE")
biofam.om <- seqdist(biofam.seq, method="OM", indel=3, sm=costs)

## normalized LCP distances
biofam.lcp <- seqdist(biofam.seq, method="LCP", norm=TRUE)

## normalized LCS distances to the most frequent sequence in the data set
biofam.lcs <- seqdist(biofam.seq,method="LCS", refseq=0, norm=TRUE)

## histogram of the normalized LCS distances
hist(biofam.lcs)