spacr.sp_stats¶
Module Contents¶
- spacr.sp_stats.choose_p_adjust_method(num_groups, num_data_points)[source]¶
Selects the most appropriate p-value adjustment method based on data characteristics.
Parameters: - num_groups: Number of unique groups being compared - num_data_points: Number of data points per group (assuming balanced groups)
Returns: - A string representing the recommended p-adjustment method
- spacr.sp_stats.perform_normality_tests(df, grouping_column, data_columns)[source]¶
Perform normality tests for each group and data column.
- spacr.sp_stats.perform_levene_test(df, grouping_column, data_column)[source]¶
Perform Levene’s test for equal variance.
- spacr.sp_stats.perform_statistical_tests(df, grouping_column, data_columns, paired=False)[source]¶
Perform statistical tests for each data column.
- spacr.sp_stats.perform_posthoc_tests(df, grouping_column, data_column, is_normal)[source]¶
Perform post-hoc tests for multiple groups with both original and adjusted p-values.
- spacr.sp_stats.chi_pairwise(raw_counts, verbose=False)[source]¶
Perform pairwise chi-square or Fisher’s exact tests between all unique group pairs and apply p-value correction.
Parameters: - raw_counts (DataFrame): Contingency table with group-wise counts. - verbose (bool): Whether to print results for each pair.
Returns: - pairwise_df (DataFrame): DataFrame with pairwise test results, including corrected p-values.