# seqdiff: Position-wise discrepancy analysis between groups of... In TraMineR: Trajectory Miner: a Toolbox for Exploring and Rendering Sequences

 seqdiff 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 = "auto"), 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` Vector of two integers: Time range of the sliding windows. Comparison at t is computed on the window (t + `cmprange`, t + `cmprange`). `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 t, the method uses `seqdist` to compute a distance matrix over the time-window (t + `cmprange`, t + `cmprange`) 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 five statistics (Pseudo F, Pseudo Fbf, Pseudo R2, Bartlett, and Levene) for each time stamp of the sequence (see `dissassoc`) `discrepancy` A `data.frame` with, at each time position t, the discrepancy within the whole set of sequences and within each group (defined by the `group` variable).

### Author(s)

Matthias Studer (with Gilbert Ritschard for the help page)

### References

Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (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. 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.

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

### Examples

```## Define a state sequence object
## First 12 months of first 100 trajectories