nimare.decode.continuous
.CorrelationDecoder
- class CorrelationDecoder(feature_group=None, features=None, frequency_threshold=0.001, meta_estimator=None, target_image='z_desc-specificity', memory_limit='1gb')[source]
Bases:
nimare.base.Decoder
Decode an unthresholded image by correlating the image with meta-analytic maps.
Changed in version 0.0.8:
[ENH] Add low-memory option to
meta_estimator
- Parameters
Warning
Coefficients from correlating two maps have very large degrees of freedom, so almost all results will be statistically significant. Do not attempt to evaluate results based on significance.
- fit(dataset, drop_invalid=True)[source]
Fit Decoder to Dataset.
- Parameters
- Returns
MetaResult
– Results of Decoder fitting.
Notes
The
fit
method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Decoders’ individual “fitting” methods are implemented as_fit
, although users should callfit
.Selection of features based on requested features and feature group is performed in
Decoder._preprocess_input
.
- 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.- Returns
self
- transform(img)[source]
Correlate target image with each feature-specific meta-analytic map.
- Parameters
img (
nibabel.nifti1.Nifti1Image
) – Image to decode. Must be in same space asdataset
.- Returns
out_df (
pandas.DataFrame
) – DataFrame with one row for each feature, an index named “feature”, and one column: “r”.