compare_groups()

compare_groups() checks which subsequences differ most across groups.

Function Usage

compare_groups(
    subsequence_results,
    group_labels,
    test_method="chisq",
    p_adjust_method=None,
    pvalue_threshold=None,
    weighted=True
)

TraMineR Parameter Mapping

• subsequence_results -> TraMineR fsub
• group_labels -> TraMineR group
• test_method -> Conceptually corresponds to the chi-square association test used by seqecmpgroup()
• pvalue_threshold -> TraMineR pvalue.limit
• weighted -> TraMineR weighted behavior

Entry Parameters

Parameter	Required	Type	Description
subsequence_results	✓	SubsequenceList	The mined subsequences to test across groups.
group_labels	✓	array-like	Group label for each sequence (same length as input sequences).
test_method	✗	str	Statistical association test used to compare subsequence presence across groups. Currently "chisq" is supported.
p_adjust_method	✗	str / None	Optional multiple-testing correction method, such as "bonferroni".
pvalue_threshold	✗	float	Keep results with p-value at or below this threshold.
weighted	✗	bool	Use sequence weights if available.

What It Does

For each subsequence, compare_groups() creates a presence/absence indicator showing whether the subsequence occurs in each sequence. It then cross-tabulates this indicator with the group labels and computes a chi-square association statistic. Subsequence patterns are then filtered by the p-value threshold, if provided, and sorted by their strength of association with the grouping variable.

Example

discriminating = compare_groups(
    subsequence_results=fsubseq,
    group_labels=df["gender"],
    pvalue_threshold=0.05
)

R Counterpart

• Closest R function: seqecmpgroup
• Mapping note: Both functions identify subsequences that best separate groups using chi-square based testing.

Notes

• group_labels length must match the number of sequences.
• scipy is required for statistical testing.

Authors

Code: Yuqi Liang

Documentation: Yuqi Liang

References

Ritschard, G., Bürgin, R., & Studer, M. (2013). Exploratory Mining of Life Event Histories. In J. J. McArdle & G. Ritschard (Eds.), Contemporary Issues in Exploratory Data Mining in the Behavioral Sciences (pp. 221-253). Routledge.