nimare.meta.esma¶

Effect-size meta-analysis functions

Functions

`fishers`(z_maps[, two_sided])	Run a Fisher’s image-based meta-analysis on z-statistics.
`rfx_glm`(con_maps[, null, n_iters, two_sided])	Run a random-effects (RFX) GLM on contrast maps.
`stouffers`(z_maps[, inference, null, …])	Run a Stouffer’s image-based meta-analysis on z-statistic maps.
`weighted_stouffers`(z_maps, sample_sizes[, …])	Run a Stouffer’s image-based meta-analysis on z-statistic maps.

fishers(z_maps, two_sided=True)[source]¶

Run a Fisher’s image-based meta-analysis on z-statistics.

Parameters:	z_maps ((n_contrasts, n_voxels) `numpy.ndarray`) – A 2D array of z-statistics. two_sided (`bool`, optional) – Whether to do a two- or one-sided test. Default is True.
Returns:	result – Dictionary containing maps for test statistics, p-values, and negative log(p) values.
Return type:	`dict`

rfx_glm(con_maps, null='theoretical', n_iters=None, two_sided=True)[source]¶

Run a random-effects (RFX) GLM on contrast maps.

Parameters:	con_maps ((n_contrasts, n_voxels) `numpy.ndarray`) – A 2D array of contrast maps in the same space, after masking. null ({'theoretical', 'empirical'}, optional) – Whether to use a theoretical null T distribution or an empirically- derived null distribution determined via sign flipping. Default is ‘theoretical’. n_iters (`int` or `None`, optional) – The number of iterations to run in estimating the null distribution. Only used if `null = 'empirical'`. two_sided (`bool`, optional) – Whether to do a two- or one-sided test. Default is True.
Returns:	result – Dictionary object containing maps for test statistics, p-values, and negative log(p) values.
Return type:	`dict`

stouffers(z_maps, inference='ffx', null='theoretical', n_iters=None, two_sided=True)[source]¶

Run a Stouffer’s image-based meta-analysis on z-statistic maps.

Parameters:	z_maps ((n_contrasts, n_voxels) `numpy.ndarray`) – A 2D array of z-statistic maps in the same space, after masking. inference ({'ffx', 'rfx'}, optional) – Whether to use fixed-effects inference (default) or random-effects inference. null ({'theoretical', 'empirical'}, optional) – Whether to use a theoretical null T distribution or an empirically- derived null distribution determined via sign flipping. Empirical null is only possible if `inference = 'rfx'`. n_iters (`int` or `None`, optional) – The number of iterations to run in estimating the null distribution. Only used if `inference = 'rfx'` and `null = 'empirical'`. two_sided (`bool`, optional) – Whether to do a two- or one-sided test. Default is True.
Returns:	result – Dictionary containing maps for test statistics, p-values, and negative log(p) values.
Return type:	`dict`

weighted_stouffers(z_maps, sample_sizes, two_sided=True)[source]¶

Run a Stouffer’s image-based meta-analysis on z-statistic maps.

Parameters:	z_maps ((n_contrasts, n_voxels) `numpy.ndarray`) – A 2D array of z-statistic maps in the same space, after masking. sample_sizes ((n_contrasts,) `numpy.ndarray`) – A 1D array of sample sizes associated with contrasts in `z_maps`. Must be in same order as rows in `z_maps`. two_sided (`bool`, optional) – Whether to do a two- or one-sided test. Default is True.
Returns:	result – Dictionary containing maps for test statistics, p-values, and negative log(p) values.
Return type:	`dict`