pymare.estimators
.Hedges
- class Hedges[source]
Bases:
BaseEstimator
Hedges meta-regression estimator.
Estimates the between-subject variance tau^2 using the Hedges and Olkin1 approach.
Notes
This estimator accepts 2-D inputs for
y
andv
–i.e., it can produce estimates simultaneously for multiple independent sets ofy
/v
values (use the 2nd dimension for the parallel iterates). TheX
matrix must be identical for all iterates.References
- 1
Larry V Hedges and Ingram Olkin. Statistical methods for meta-analysis. Academic press, 2014.
- fit(y, v, X)[source]
Fit the estimator to data.
- Parameters
y (
numpy.ndarray
of shape (n, d)) – The dependent variable(s) (y).v (
numpy.ndarray
of shape (n, d)) – Sampling variances.X (
numpy.ndarray
of shape (n, p)) – The independent variable(s) (X).
- Return type
- fit_dataset(dataset, *args, **kwargs)[source]
Apply the current estimator to the passed Dataset container.
A convenience interface that wraps fit() and automatically aligns the variables held in a Dataset with the required arguments.
- Parameters
dataset (
Dataset
) – A PyMARE Dataset instance holding the data.*args – Optional positional arguments to pass onto the
fit()
method.**kwargs – Optional keyword arguments to pass onto the
fit()
method.
- get_v(dataset)[source]
Get the variances, or an estimate thereof, from the given Dataset.
- Parameters
dataset (
Dataset
) – The dataset to use to retrieve/estimate v.- Returns
2-dimensional array of variances/variance estimates.
- Return type
Notes
This is equivalent to directly accessing
dataset.v
when variances are present, but affords a way of estimating v from sample size (n) for any estimator that implicitly estimates a sigma^2 parameter.