Estimators SubPackage

This subpackage implements the various estimators

transitionMatrix.estimators.simple_estimator module

class transitionMatrix.estimators.simple_estimator.SimpleEstimator(states=None, ci=None)

Bases: BaseEstimator

Class for implementing a simple estimator suitable for single period transitions

This is useful for testing, getting a first feel about the transition landscape.

fit(data)

Parameters:

data (array-like) – The data to use for the estimation

Returns:

• matrix (estimated transition matrix)

• confint_lower (lower confidence interval)

• confint_upper (upper confidence interval)

Notes

• loop over data rows

• expected format is (id, state_in, state_out)

• calculate population count N^i_k per state i

• calculate migrations count N^{ij}_{kl} from i to j

• calculate transition matrix as ratio T^{ij}_{kl} = N^{ij}_{kl} / N^i_k

transitionMatrix.estimators.cohort_estimator module

class transitionMatrix.estimators.cohort_estimator.CohortEstimator(cohort_bounds=None, states=None, ci=None)

Bases: BaseEstimator

Class for implementing a Cohort Estimator for the transition matrix

Documentation: Cohort Estimator

fit(data, labels=None)

Parameters:

• data (dataframe - The data to use for the estimation (in sorted by ID in compact format))

• labels (an optional dictionary for relabeling column names)

Returns:

matrix_set

Return type:

An estimated transition matrix set

Notes

• loop over data rows (id, timepoint, state)

• at least two distinct timepoints are required (initial and final)

• calculate population count N^i_k per state i per timepoint k

• calculate migrations count N^{ij}_{kl} from i to j from timepoint k to timepoint l

• calculate transition matrix as ratio T^{ij}_{kl} = N^{ij}_{kl} / N^i_k

• calculate also count-averaged matrix

References

get_average()

transitionMatrix.estimators.aalen_johansen_estimator module

class transitionMatrix.estimators.aalen_johansen_estimator.AalenJohansenEstimator(states=None)

Bases: DurationEstimator

Class for implementing the Aalen-Johansen estimator for the transition matrix

Documentation: Aalen-Johansen Estimator

fit(data, labels=None)

Parameters:

• data (dataframe - The data to use for the estimation provided in a pandas data frame in long format,with one row per observed transition. The data frame must contain the following columns (or pass a label object that will assign accordingly:) –

  • ID: A unique entity identification number

  • TIME Time when a transition occurs

  • FROM: State from where a transition occurs

  • TO: State to which a transition occurs

• labels (an optional dictionary for relabeling column names if those deviate from the convention) –

  • TODO constraint possible transitions (absorbing states)

  • TODO censored data

  • TODO partial dates

  • TODO covariance calculation

  • TODO confidence intervals

Returns:

• etm.values (estimated empirical transition matrix throughout the observed interval. This is a three dimensional array object (From State, To State, Timepoint))

• observation_times (a list of observation times etm.observation_times)

• TODO Store counts as well as frequencies

• TODO Optional Binning of close observation times

Note

The input data MUST be pre-sorted in ascending time order. This is easily done using pandas functionality.

References

transitionMatrix.estimators.kaplan_meier_estimator module

Todo

This is future functionality

class transitionMatrix.estimators.kaplan_meier_estimator.KaplanMeierEstimator(cohort_intervals=None, states=None)

Bases: DurationEstimator