gemmr.sample_size.linear_model.cca_req_corr
- gemmr.sample_size.linear_model.cca_req_corr(X, Y, ax, ay, n_req, criterion='combined', algorithm='linear_model', target_power=0.9, target_error=0.1, expl_var_ratio=0.3, data_home=None)
Determines the minimum required true correlation to achieve power and error levels.
- Parameters:
X (np.ndarray (n_samples, n_X_features) or int >= 2) – either a data matrix or directly the number of features for data matrix \(X\)
Y (np.ndarray (n_samples, n_Y_features) or int >= 2) – either a data matrix or directly the number of features for data matrix \(Y\)
ax (float < 0 or None) – principal component spectrum decay constant, if
X
is not a data matrix,None
otherwiseay (float < 0 or None) – principal component spectrum decay constant, if
Y
is not a data matrix,None
otherwisen_req (sample_size) – available sample size
criterion (str) –
criterion according to which sample sizes are estimated. Can be:
'combined'
'power'
'association_strength'
'weight'
'score'
'loading'
'crossloading'
algorithm (str) –
algorithm used to calculate sample sizes. Can be:
'linear_model'
target_power (float between o and 1) – if
criterion
is'combined'
or'power'
sample size is chosen to obtain at leasttarget_power
powertarget_error (float between 0 and 1) – if criterion is not
'power'
sample size is chosen to obtain at mosttarget_error
error in error metric(s)expl_var_ratio (float) – if
X
orY
is a data matrix,ax
oray
, respectively, will be estimated directly from the data using the number of principal components that explain this amount of variancedata_home (None or str) – path where outcome data are stored,
None
indicates default path
- Returns:
req_corr – minimum required true correlation
- Return type:
float