mosum.classes
¶
Module Contents¶
Classes¶
mosum object |
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multiscale_cpts object |
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multiscale_cpts object |
- class mosum.classes.mosum_obj(x, G_left, G_right, var_est_method, var_custom, boundary_extension, stat, unscaledStatistic, var_estimation, threshold, alpha, threshold_custom, threshold_value, criterion, eta, epsilon, cpts, cpts_info, do_confint, ci)¶
mosum object
- plot(display=['data', 'mosum'][0], cpts_col='red', critical_value_col='blue', xlab='Time')¶
plot method - plots data or detector
- summary()¶
summary method
- print()¶
print method
- confint(parm: str = 'cpts', level: float = 0.05, N_reps: int = 1000)¶
Generate bootstrap confidence intervals for change points
- Parameters:
parm (Str) – unused
level (float) – numeric value in (0, 1), such that the 100(1-level)% confidence bootstrap intervals are computed
N_reps (int) – number of bootstrap replicates
- Return type:
dictionary containing inputs, pointwise intervals and uniform intervals
- class mosum.classes.multiscale_cpts(x, cpts, cpts_info, pooled_cpts, G, alpha, threshold, threshold_function, criterion, eta, do_confint, ci)¶
multiscale_cpts object
- plot(display=['data', 'mosum'][0], cpts_col='red', critical_value_col='blue', xlab='Time')¶
plot method - plots data or detector
- summary()¶
summary method
- print()¶
print method
- confint(parm: str = 'cpts', level: float = 0.05, N_reps: int = 1000)¶
Generate bootstrap confidence intervals for change points
- Parameters:
parm (Str) – unused
level (float) – numeric value in (0, 1), such that the 100(1-level)% confidence bootstrap intervals are computed
N_reps (int) – number of bootstrap replicates
- Return type:
dictionary containing inputs, pointwise intervals and uniform intervals
- class mosum.classes.multiscale_cpts_lp(x, cpts, cpts_info, pooled_cpts, G, alpha, threshold, threshold_function, criterion, eta, epsilon, sc, rule, penalty, pen_exp, do_confint, ci)¶
Bases:
multiscale_cpts
multiscale_cpts object
- plot(display=['data', 'significance'][0], shaded=['CI', 'bandwidth', 'none'][0], level=0.05, N_reps=1000, CI=['pw', 'unif'][0], xlab='Time')¶
plot method - plots data or p-values, shaded according to confidence intervals or detection bandwidth