R: Position-wise discrepancy analysis between groups of...

seqdiff {TraMineR} | R Documentation

## Position-wise discrepancy analysis between groups of sequences

### Description

The function analyses how the differences between groups of sequences evolve along the positions. It runs a sequence of discrepancy analyses on sliding windows.

### Usage

    seqdiff(seqdata, group, cmprange = c(0, 1),
      seqdist.args = list(method = "LCS", norm = TRUE), with.missing = FALSE,
      weighted = TRUE, squared = FALSE, seqdist_arg)

### Arguments

`seqdata` | a state sequence object created with the `seqdef` function.
`group` | The group variable.
`cmprange` | The time range of the sliding window on which subsequences are compared.
`seqdist.args` | List of arguments passed to `seqdist` for computing the distances.
`with.missing` | Logical. If `TRUE`, missing values are considered as an additional state. If `FALSE` subsequences with missing values are removed from the analysis.
`weighted` | Logical. If `TRUE`, `seqdiff` uses the weights specified in `seqdata`.
`squared` | Logical. If `TRUE` the dissimilarities are squared for computing the discrepancy.
`seqdist_arg` | Deprecated. Use `seqdist.args` instead.

### Details

The function analyses how the part of discrepancy explained by the `group` variable evolves along the position axis. It runs successively discrepancy analyses within a sliding time-window of range `cmprange`). At each position, the method uses `seqdist` to compute a distance matrix over the time-window and then derives the explained discrepancy on that window with `dissassoc`.

There are print and plot methods for the returned value.

### Value

A `seqdiff` object, with the following items:

`stat` | A `data.frame` with three statistics (PseudoF, PseudoR2 and PseudoT) for each time stamp of the sequence, see `dissassoc`
`discrepancy` | A `data.frame` with, at each time stamp, the discrepancy within each group defined by the `group` variable and for the whole population.

### Author(s)

Matthias Studer (with Gilbert Ritschard for the help page)

### References

Studer, M., G. Ritschard, A. Gabadinho and N. S. M\"uller (2011). Discrepancy analysis of state sequences, Sociological Methods and Research, Vol. 40(3), 471-510, doi: 10.1177/0049124111415372.

Studer, M., G. Ritschard, A. Gabadinho and N. S. M\"uller (2010) Discrepancy analysis of complex objects using dissimilarities. In F. Guillet, G. Ritschard, D. A. Zighed and H. Briand (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence, Volume 292, pp. 3-19. Berlin: Springer.

Studer, M., G. Ritschard, A. Gabadinho and N. S. M\"uller (2009) Analyse de dissimilarit\'es par arbre d'induction. In EGC 2009, Revue des Nouvelles Technologies de l'Information, Vol. E-15, pp. 7-18.

### See Also

`dissassoc` to analyse the association of the `group` variable with the whole sequence

### Examples

    ## Define a state sequence object
    data(mvad)
    ## First 12 months of first 100 trajectories
    mvad.seq <- seqdef(mvad[1:100, 17:28])

    ## Position-wise discrepancy analysis
    mvad.diff <- seqdiff(mvad.seq, group=mvad$gcse5eq[1:100])
    print(mvad.diff)
    plot(mvad.diff, stat=c("Pseudo R2", "Levene"), xtstep=6)
    plot(mvad.diff, stat="discrepancy")

[Package TraMineR version 2.2-2 Index]