gemmr.sample_size.linear_model.fit_linear_model
- gemmr.sample_size.linear_model.fit_linear_model(criterion, model, estr=None, tag=None, target_power=0.9, target_error=0.1, data_home=None, include_pc_var_decay_constants=None, include_latent_explained_vars=None)
Fit a linear model to outcome data.
- Parameters:
criterion (str) –
Can be:
'combined'
'power'
'association_strength'
'weight'
'score'
'loading'
'crossloading'
model (str) – ‘cca’ or ‘pls’
estr (None or sklearn-style estimator instance) – if not
None
must be compatible withmodel
tag (str or None) – further specifies the outcome data file, cf.
data.load_outcomes()
target_error (float between 0 and 1) – target error level
target_power (float between 0 and 1) – target power level
data_home (None or str) – path where outcome data are stored,
None
indicates default pathinclude_pc_var_decay_constants (bool) – whether to include a predictor for the principal component spectrum decay constant in the linear model
include_latent_explained_vars (bool) – whether to include a predictor for the latent explained variance in the linear model
- Returns:
lm – fitted model
- Return type:
sklearn.LinearRegression instance