cedalion.models.glm.solve

Solve the GLM model.

Functions

fit(ts, design_matrix[, ...])

Fit design matrix to data.

iter_design_matrix(ts, design_matrix[, ...])

Iterate over the design matrix and yield the design matrix for each group.

predict(ts, thetas, design_matrix[, ...])

Predict time series from design matrix and thetas.
cedalion.models.glm.solve.fit(
ts: cdt.NDTimeSeries,
design_matrix: DataArray,
channel_wise_regressors: list[DataArray] | None = None,
noise_model='ols',
)[source]

Fit design matrix to data.

Parameters:

  • ts – the time series to be modeled

  • design_matrix – DataArray with dims time, regressor, chromo

  • channel_wise_regressors – optional list of design matrices, with additional channel dimension

  • noise_model – must be ‘ols’ for the moment

Returns:

thetas as a DataArray

cedalion.models.glm.solve.predict(
ts: cdt.NDTimeSeries,
thetas: DataArray,
design_matrix: DataArray,
channel_wise_regressors: list[DataArray] | None = None,
) → cdt.NDTimeSeries[source]

Predict time series from design matrix and thetas.

Parameters:

  • ts (cdt.NDTimeSeries) – The time series to be modeled.

  • thetas (xr.DataArray) – The estimated parameters.

  • design_matrix (xr.DataArray) – DataArray with dims time, regressor, chromo

  • channel_wise_regressors (list[xr.DataArray]) – Optional list of design matrices,

  • dimension. (with additional channel)

Returns:

The predicted time series.

Return type:

prediction (xr.DataArray)

cedalion.models.glm.solve.iter_design_matrix(
ts: cdt.NDTimeSeries,
design_matrix: DataArray,
channel_wise_regressors: list[DataArray] | None = None,
channel_groups: list[int] | None = None,
)[source]

Iterate over the design matrix and yield the design matrix for each group.

Parameters:

  • ts (cdt.NDTimeSeries) – The time series to be modeled.

  • design_matrix (xr.DataArray) – DataArray with dims time, regressor, chromo.

  • channel_wise_regressors (list[xr.DataArray] | None, optional) – Optional list of design matrices, with additional channel dimension.

  • channel_groups (list[int] | None, optional) – Optional list of channel groups.

Yields:

tuple –

A tuple containing:

  • dim3 (str): The third dimension name.

  • group_y (cdt.NDTimeSeries): The grouped time series.

  • group_design_matrix (xr.DataArray): The grouped design matrix.