pymare.estimators.SampleSizeBasedLikelihoodEstimator

class SampleSizeBasedLikelihoodEstimator(method='ml', **kwargs)

Bases: BaseEstimator

Likelihood-based estimator for data with known sample sizes but unknown sampling variances.

Iteratively estimates the between-subject variance tau^2 and fixed effect betas using the specified likelihood-based estimator (ML or REML) [1].

Parameters:

• method ({"ML", "REML"}, optional) – The estimation method to use. Either ‘ML’ (for maximum-likelihood) or ‘REML’ (restricted maximum-likelihood). Default = ‘ML’.

• **kwargs – Keyword arguments to pass to the SciPy minimizer.

Notes

Homogeneity of sigma^2 across studies is assumed.

The ML and REML solutions are obtained via SciPy’s scalar function minimizer (scipy.optimize.minimize()). Parameters to minimize() can be passed in as keyword arguments.

References

[1]

Patarawan Sangnawakij, Dankmar Böhning, Sa-Aat Niwitpong, Stephen Adams, Michael Stanton, and Heinz Holling. Meta-analysis without study-specific variance information: heterogeneity case. Statistical Methods in Medical Research, 28(1):196–210, 2019. URL: https://doi.org/10.1177/0962280217718867, doi:10.1177/0962280217718867.

fit(y, n, X)

Fit the estimator to data.

Parameters:

• y (numpy.ndarray of shape (n, d)) – The dependent variable(s) (y).

• n (numpy.ndarray of shape (n, d)) – Sample sizes.

• X (numpy.ndarray of shape (n, p)) – The independent variable(s) (X).

Return type:

SampleSizeBasedLikelihoodEstimator

fit_dataset(dataset, *args, **kwargs)

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)

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:

numpy.ndarray

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.

summary()

Generate a MetaRegressionResults object for the fitted estimator.

Return type:

MetaRegressionResults

Examples using pymare.estimators.SampleSizeBasedLikelihoodEstimator

Run Estimators on a simulated dataset

Run Estimators on a simulated dataset