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Returns
-------
:obj:`nimare.results.MetaResult`
Results of Estimator fitting.
"""
self._validate_input(dataset)
self._preprocess_input(dataset)
maps = self._fit(dataset)
if hasattr(self, 'masker') and self.masker is not None:
masker = self.masker
else:
masker = dataset.masker
self.results = MetaResult(self, masker, maps)
return self.results
Cluster-level FWE-corrected z-statistic maps associated with the
respective models.
ma_maps1 : (E x V) array_like or None, optional
Experiments by voxels array of modeled activation
values. If not provided, MA maps will be generated from dataset1.
ma_maps2 : (E x V) array_like or None, optional
Experiments by voxels array of modeled activation
values. If not provided, MA maps will be generated from dataset2.
Returns
-------
:obj:`nimare.results.MetaResult`
Results of ALE subtraction analysis.
"""
maps = self._fit(ale1, ale2, image1, image2, ma_maps1, ma_maps2)
self.results = MetaResult(self, ale1.mask, maps)
return self.results
Fit Estimator to datasets.
Parameters
----------
dataset/dataset2 : :obj:`nimare.dataset.Dataset`
Dataset objects to analyze.
Returns
-------
:obj:`nimare.results.MetaResult`
Results of Estimator fitting.
"""
self._validate_input(dataset)
self._validate_input(dataset2)
maps = self._fit(dataset, dataset2)
self.results = MetaResult(self, dataset.masker.mask_img, maps)
return self.results
iter_diff_values[:, voxel],
tail='upper')
grp2_z_arr = p_to_z(grp2_p_arr, tail='one')
# Unmask
grp2_z_map = np.zeros(grp2_voxel.shape[0])
grp2_z_map[:] = np.nan
grp2_z_map[grp2_voxel] = grp2_z_arr
# Fill in output map
diff_z_map = np.zeros(image1.shape[0])
diff_z_map[grp2_voxel] = -1 * grp2_z_map[grp2_voxel]
# could overwrite some values. not a problem.
diff_z_map[grp1_voxel] = grp1_z_map[grp1_voxel]
images = {'grp1-grp2_z': diff_z_map}
self.results = MetaResult(self, self.mask, maps=images)
def _validate_input(self, result):
if not isinstance(result, MetaResult):
raise ValueError("First argument to transform() must be an "
"instance of class MetaResult, not {}."
.format(type(result)))
if self.method not in self._native_methods:
raise ValueError("Unsupported {} correction method: {}".format(
self._correction_method, self.method))
for rm in self._required_maps:
if result.maps.get(rm) is None:
raise ValueError("{0} requires {1} maps to be present in the "
"MetaResult, but none were found."
.format(type(self), rm))