nimare.diagnostics
.Jackknife
- class Jackknife(target_image='z_desc-size_level-cluster_corr-FWE_method-montecarlo', voxel_thresh=None, n_cores=1)[source]
Bases:
nimare.base.NiMAREBase
Run a jackknife analysis on a meta-analysis result.
New in version 0.0.11.
- Parameters
target_image (
str
, optional) – The meta-analytic map for which clusters will be characterized. The default is z because log-p will not always have value of zero for non-cluster voxels.voxel_thresh (
float
or None, optional) – An optional voxel-level threshold that may be applied to thetarget_image
to define clusters. This can be None if thetarget_image
is already thresholded (e.g., a cluster-level corrected map). Default is None.n_cores (
int
, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is 1.
Notes
This analysis characterizes the relative contribution of each experiment in a meta-analysis to the resulting clusters by looping through experiments, calculating the Estimator’s summary statistic for all experiments except the target experiment, dividing the resulting test summary statistics by the summary statistics from the original meta-analysis, and finally averaging the resulting proportion values across all voxels in each cluster.
Warning
Pairwise meta-analyses, like ALESubtraction and MKDAChi2, are not yet supported in this method.
Methods
get_params
([deep])Get parameters for this estimator.
load
(filename[, compressed])Load a pickled class instance from file.
save
(filename[, compress])Pickle the class instance to the provided file.
set_params
(**params)Set the parameters of this estimator.
transform
(result)Apply the analysis to a MetaResult.
- classmethod load(filename, compressed=True)[source]
Load a pickled class instance from file.
- Parameters
- Returns
obj – Loaded class object.
- Return type
class object
- set_params(**params)[source]
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Return type
self
- transform(result)[source]
Apply the analysis to a MetaResult.
- Parameters
result (
MetaResult
) – A MetaResult produced by a coordinate- or image-based meta-analysis.- Returns
contribution_table (
pandas.DataFrame
) – A DataFrame with information about relative contributions of each experiment to each cluster in the thresholded map. There is one row for each experiment, as well as one more row at the top of the table (below the header), which has the center of mass of each cluster. The centers of mass are not guaranteed to fall within the actual clusters, but can serve as a useful heuristic for identifying them. There is one column for each cluster, with column names being integers indicating the cluster’s associated value in thelabeled_cluster_img
output.labeled_cluster_img (
nibabel.nifti1.Nifti1Image
) – The labeled, thresholded map that is used to identify clusters characterized by this analysis. Each cluster in the map has a single value, which corresponds to the cluster’s column name incontribution_table
.