R: Identifying discriminating subsequences

seqecmpgroup {TraMineR} | R Documentation

## Identifying discriminating subsequences

### Description

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

### Usage

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

### Arguments

`subseq` | A `subseqelist` object (list of subsequences) such as produced by `seqefsub`
`group` | Group membership, i.e., a variable or factor defining the groups which we want to discriminate
`method` | The discrimination method; one of `"bonferroni"` or `"chisq"`
`pvalue.limit` | Can be used to filter the results. Only subsequences with a p-value lower than this parameter are selected. If `NULL` all subsequences are returned (regardless of their p-values).
`weighted` | Logical. If `TRUE`, `seqecmpgroup` uses the weights specified in `subseq`, (see `seqefsub`).

### Details

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.

### Value

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 `seqeconstraint`)
`labels` | Levels (value labels) of the target group variable
`type` | Type of test used
`data` | A data frame with columns support, index (original rank of the subsequence, i.e., its position in the inputted `subseq`) and a pair of frequency and Pearson residual columns for each group

### Author(s)

Matthias Studer (with Gilbert Ritschard for the help page)

### References

Studer, M., Müller, N.S., Ritschard, G. & Gabadinho, A. (2010), "Classer, discriminer et visualiser des séquences d'événements", In Extraction et gestion des connaissances (EGC 2010), Revue des nouvelles technologies de l'information RNTI. Vol. E-19, pp. 37-48.

Ritschard, G., Bürgin, R., and Studer, M. (2014), "Exploratory Mining of Life Event Histories", In McArdle, J.J. & Ritschard, G. (eds) Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences. Series: Quantitative Methodology, pp. 221-253. New York: Routledge.

### See Also

See also `plot.subseqelistchisq` to plot the results

### Examples

    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 the six most discriminating subsequences
    print(discr[1:6])
    ##Plotting the six most discriminating subsequences
    plot(discr[1:6])

[Package TraMineR version 2.2-3 Index]