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

Identify and sort the most discriminating subsequences by their discriminating power.

1 2 | ```
seqecmpgroup(subseq, group, method="chisq", pvalue.limit=NULL,
weighted = TRUE)
``` |

`subseq` |
A |

`group` |
Group membership, i.e., a variable or factor defining the groups which we want to discriminate |

`method` |
The discrimination method; one of |

`pvalue.limit` |
Can be used to filter the results. Only subsequences with a p-value lower than this parameter are selected. If |

`weighted` |
Logical. If |

The following discrimination test functions are implemented:
`chisq`

, the Pearson Independence Chi-squared test, and
`bonferroni`

, the Pearson Independence Chi-squared test with Bonferroni correction.

An objet of type `subseqelistchisq`

(subtype of `subseqelist`

) with the following elements

`subseq` |
Sorted list of found discriminating subsequences |

`eseq` |
The event sequence object on which the tests were computed |

`constraint` |
Time constraints used for searching the subsequences (see |

`labels` |
Levels (value labels) of the target group variable |

`type` |
Type of test used |

`data` |
A data frame with columns support, index (original order of the subsequence) and a pair of frequency and Pearson residual columns for each group |

Matthias Studer (with Gilbert Ritschard for the help page)

Studer, M., M<c3><bc>ller, N.S., Ritschard, G. & Gabadinho, A. (2010), "Classer, discriminer et visualiser des s<c3><a9>quences d'<c3><a9>v<c3><a9>nements", In Extraction et gestion des connaissances (EGC 2010), *Revue des nouvelles technologies de l'information* RNTI. Vol. E-19, pp. 37-48.

See also `plot.subseqelistchisq`

to plot the results

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
data(actcal.tse)
actcal.eseq <- seqecreate(actcal.tse)
##Searching for frequent subsequences, that is, appearing at least 20 times
fsubseq <- seqefsub(actcal.eseq, pmin.support=0.01)
##searching for susbsequences discriminating the most men and women
data(actcal)
discr <- seqecmpgroup(fsubseq, group=actcal$sex, method="bonferroni")
##Printing discriminating subsequences
print(discr)
##Plotting the six most discriminating subsequences
plot(discr[1:6])
``` |

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