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