Description Usage Arguments Details References See Also Examples

This funciton propose a graphical representation of a fuzzy clustering results where sequences are weighted according to their cluster membership strength.

1 2 |

`seqdata` |
State sequence object created with the |

`group` |
A fuzzy partition of the data, either as a membership matrix or as a |

`membership.threashold` |
Numeric. Minimum membership strength to be included in plots. |

`type` |
the type of the plot. Available types are |

`members.weighted` |
Logical. Should the sequences be weighted by their membership strength in each group before being plotted? |

`memb.exp` |
Optional. Fuzzyness parameter used in the |

`...` |
arguments to be passed to |

The dataset is augmented by repeating the sequence *s_i* of individual *i* *k* times (i.e., once per cluster). We therefore have *k* sequences for individual *i*, denoted as *s_{i1}... s_{ik}*. These sequences are therefore weighted according to their membership degree *u_{i1}... u_{ik}*. Hence, even if the same sequence were repeated *k* times, its total weight sum to 1. An additional categorical covariate is created in this augmented dataset that specifies the cluster (ranging from 1 to *k*) of the associated membership degree. This weighting strategy allows us to use any tools available for weighted sequence data (see `seqplot`

).

For index plots, we additionally suggest ordering the sequences according to membership degree by setting `sortv="membership"`

(see example). The most typical sequence lies at the top of the subfigures, with a high membership degree; meanwhile, the bottom shows less-characteristic patterns. Restricting to sequences with the highest membership degree can be achieved with the `membership.treashold`

argument.

Studer, M. (2018). Divisive property-based and fuzzy clustering for sequence analysis. In G. Ritschard and M. Studer (Eds.), *Sequence Analysis and Related Approaches: Innovative Methods and Applications*, Life Course Research and Social Policies.

See also `fanny`

for fuzzy clustering.

1 2 3 4 5 6 7 8 9 10 11 | ```
data(mvad)
mvad.seq <- seqdef(mvad[1:100, 17:86])
## COmpute distance using Hamming distance
diss <- seqdist(mvad.seq, method="HAM")
library(cluster)
fclust <- fanny(diss, k=2, diss=TRUE)
fuzzyseqplot(mvad.seq, group=fclust, type="d")
fuzzyseqplot(mvad.seq, group=fclust, type="I", sortv="membership")
fuzzyseqplot(mvad.seq, group=fclust, type="f")
``` |

```
Loading required package: TraMineR
TraMineR stable version 2.0-11.1 (Built: 2019-05-12)
Website: http://traminer.unige.ch
Please type 'citation("TraMineR")' for citation information.
Loading required package: cluster
This is WeightedCluster stable version 1.4 (Built: 2019-05-11)
To get the manuals, please run:
vignette("WeightedCluster") ## Complete manual in English
vignette("WeightedClusterFR") ## Complete manual in French
vignette("WeightedClusterPreview") ## Short preview in English
To cite WeightedCluster in publications please use:
Studer, Matthias (2013). WeightedCluster Library Manual: A practical
guide to creating typologies of trajectories in the social sciences
with R. LIVES Working Papers, 24. doi:
10.12682/lives.2296-1658.2013.24
[>] 6 distinct states appear in the data:
1 = FE
2 = HE
3 = employment
4 = joblessness
5 = school
6 = training
[>] state coding:
[alphabet] [label] [long label]
1 FE FE FE
2 HE HE HE
3 employment employment employment
4 joblessness joblessness joblessness
5 school school school
6 training training training
[>] 100 sequences in the data set
[>] min/max sequence length: 70/70
[>] 100 sequences with 6 distinct states
[>] creating a 'sm' with a single substitution cost of 1
[>] creating 6x6 substitution-cost matrix using 1 as constant value
[>] 91 distinct sequences
[>] min/max sequence length: 70/70
[>] computing distances using the HAM metric
[>] elapsed time: 0.042 secs
```

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