nimare.meta.cbma.mkda
.MKDADensity¶
-
class
MKDADensity
(kernel_transformer=<class 'nimare.meta.cbma.kernel.MKDAKernel'>, **kwargs)[source]¶ Multilevel kernel density analysis- Density analysis.
- Parameters
kernel_transformer (
nimare.meta.cbma.kernel.KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is MKDAKernel.**kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix ‘kernel__’ in the variable name.
Notes
Available correction methods:
MKDADensity.correct_fwe_montecarlo
References
Wager, Tor D., Martin Lindquist, and Lauren Kaplan. “Meta-analysis of functional neuroimaging data: current and future directions.” Social cognitive and affective neuroscience 2.2 (2007): 150-158. https://doi.org/10.1093/scan/nsm015
-
correct_fwe_montecarlo
(result, voxel_thresh=0.01, n_iters=1000, n_cores=-1)[source]¶ Perform FWE correction using the max-value permutation method. Only call this method from within a Corrector.
- Parameters
result (
nimare.results.MetaResult
) – Result object from a KDA meta-analysis.voxel_thresh (
float
, optional) – Cluster-defining OF-value threshold. Default is 0.01.n_iters (
int
, optional) – Number of iterations to build the vFWE and cFWE null distributions. Default is 1000.n_cores (
int
, optional) – Number of cores to use for parallelization. If <=0, defaults to using all available cores. Default is -1.
- Returns
images (
dict
) – Dictionary of 1D arrays corresponding to masked images generated by the correction procedure. The following arrays are generated by this method: ‘vthresh’, ‘logp_level-cluster’, and ‘logp_level-voxel’.
See also
nimare.correct.FWECorrector()
The Corrector from which to call this method.
Examples
>>> meta = MKDADensity() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', voxel_thresh=0.01, n_iters=5, n_cores=1) >>> cresult = corrector.transform(result)
-
fit
(dataset)[source]¶ Fit Estimator to Dataset.
- Parameters
dataset (
nimare.dataset.Dataset
) – Dataset object to analyze.- Returns
nimare.results.MetaResult
– Results of Estimator fitting.
Notes
The
fit
method is a light wrapper that runs input validation and preprocessing before fitting the actual model. Estimators’ individual “fitting” methods are implemented as_fit
, although users should callfit
.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params (mapping of string to any) – Parameter names mapped to their values.