Description Usage Arguments Details Value Author(s) References See Also Examples

Computes the complexity index, a composite measure of sequence complexity. The index uses the number of transitions in the sequence as a measure of the complexity induced by the state ordering and the longitudinal entropy as a measure of the complexity induced by the state distribution in the sequence.

1 2 |

`seqdata` |
a sequence object as returned by the the |

`with.missing` |
if set to |

The *complexity index* *C(s)* of a sequence
*s* is

*
C(s) = ([q(s) h(s)]/[q_{max} h_{max}])^(.5)*

where *q(s)* is the number of transitions in the sequence,
*q_{max}* the maximum number of transitions,
*h(s)* the within entropy, and *h_{max}* the theoretical maximum
entropy which is *h_max = -log 1/|A|*.

The index *C(s)* is the geometric mean of its two components
which are normalized. The minimum value of 0 can only be reached by a
sequence made of one distinct state, containing thus 0 transitions
and having an entropy of 0. The maximum 1 of *C(s)* is reached
when the two following conditions are fulfilled: i) Each of the state
in the alphabet is present in the sequence and the total durations
are uniform, that is, equal to *l/a* and ii) The number
of transitions in the sequence is equal to *l-1*, that
is, the length *l_d* of the DSS is equal to the length of
the sequence *l*

a vector of length equal to the number of sequences in
`seqdata`

containing the complexity index value of each
sequence.

Alexis Gabadinho (with Gilbert Ritschard for the help page)

Gabadinho, A., G. Ritschard, N. S. M<c3><bc>ller and M. Studer (2011). Analyzing and Visualizing State Sequences in R with TraMineR. *Journal of Statistical Software* **40**(4), 1-37.

Gabadinho, A., Ritschard, G., Studer, M. and M<c3><bc>ller,
N.S. (2010). "Indice de complexit<c3><a9> pour le tri et la comparaison de
s<c3><a9>quences cat<c3><a9>gorielles", In *Extraction et gestion des
connaissances (EGC 2010), Revue des nouvelles technologies de
l'information RNTI*. Vol. E-19, pp. 61-66.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## Creating a sequence object from the mvad data set
data(mvad)
mvad.labels <- c("employment", "further education", "higher education",
"joblessness", "school", "training")
mvad.scodes <- c("EM","FE","HE","JL","SC","TR")
mvad.seq <- seqdef(mvad, 15:86, states=mvad.scodes, labels=mvad.labels)
##
mvad.ci <- seqici(mvad.seq)
summary(mvad.ci)
hist(mvad.ci)
## Example using with.missing argument
data(ex1)
ex1.seq <- seqdef(ex1, 1:13)
seqici(ex1.seq)
seqici(ex1.seq, with.missing=TRUE)
``` |

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