TraMineR: seqdiff – R documentation

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seqdiff

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. Müller (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. Müller (2009) Analyse de dissimilarités par arbre d'induction. In EGC 2009, Revue des Nouvelles Technologies de l'Information, Vol. E-15, pp. 7-18.

Examples

## Define a state sequence object
data(mvad)
## First 12 months of first 200 trajectories
mvad.seq <- seqdef(mvad[1:200, 17:28])

## Position-wise discrepancy analysis
mvad.diff <- seqdiff(mvad.seq, group=mvad$gcse5eq[1:200])
print(mvad.diff)
plot(mvad.diff, stat=c("Pseudo R2", "Levene"), xtstep=6)
plot(mvad.diff, stat="discrepancy")

TraMineR

Trajectory Miner: a Toolbox for Exploring and Rendering Sequences

v2.2-1

GPL (>= 2)

Authors

Alexis Gabadinho [aut, cph], Matthias Studer [aut, cph] (<https://orcid.org/0000-0002-6269-1412>), Nicolas M"{u}ller [aut], Reto B"{u}rgin [aut], Pierre-Alexandre Fonta [aut], Gilbert Ritschard [aut, cre, cph] (<https://orcid.org/0000-0001-7776-0903>)

Initial release

2020-10-24