Statistical¶

class samplesizelib.linear.statistical.LagrangeEstimator(statmodel, **kwards)[source]¶

Description of Lagrange Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
ind_u (numpy.ndarray) – to do
epsilon (float) – to do
alpha (float) – to do
beta (float) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict

class samplesizelib.linear.statistical.LikelihoodRatioEstimator(statmodel, **kwards)[source]¶

Description of Likelihood Ratio Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
ind_u (numpy.ndarray) – to do
epsilon (float) – to do
alpha (float) – to do
beta (float) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict

class samplesizelib.linear.statistical.WaldEstimator(statmodel, **kwards)[source]¶

Description of Wald Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
ind_u (numpy.ndarray) – to do
epsilon (float) – to do
alpha (float) – to do
beta (float) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict