nimare.meta.cbma.ale
.ALE¶
-
class
ALE
(kernel_transformer=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)[source]¶ Activation likelihood estimation
- Parameters
kernel_transformer (
nimare.meta.cbma.kernel.KernelTransformer
, optional) – Kernel with which to convolve coordinates from dataset. Default is ALEKernel.**kwargs – Keyword arguments. Arguments for the kernel_transformer can be assigned here, with the prefix ‘kernel__’ in the variable name. Another optional argument is
mask
.
- Variables
masker –
inputs_ (
dict
) – Inputs to the Estimator. For CBMA estimators, there is only one key: coordinates. This is an edited version of the dataset’s coordinates DataFrame.null_distributions_ (
dict
ornumpy.ndarray
) – Null distributions for ALE and any multiple-comparisons correction methods. Entries are added to this attribute if and when the corresponding method is fit.
Notes
The ALE algorithm was originally developed in 1, then updated in 2 and 3.
Available correction methods:
ALE.correct_fwe_montecarlo
References
- 1
Turkeltaub, Peter E., et al. “Meta-analysis of the functional neuroanatomy of single-word reading: method and validation.” Neuroimage 16.3 (2002): 765-780.
- 2
Turkeltaub, Peter E., et al. “Minimizing within‐experiment and within‐group effects in activation likelihood estimation meta‐analyses.” Human brain mapping 33.1 (2012): 1-13.
- 3
Eickhoff, Simon B., et al. “Activation likelihood estimation meta-analysis revisited.” Neuroimage 59.3 (2012): 2349-2361.
-
correct_fwe_montecarlo
(result, voxel_thresh=0.001, n_iters=10000, 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 an ALE meta-analysis.voxel_thresh (
float
, optional) – Cluster-defining uncorrected p-value threshold. Default is 0.001.n_iters (
int
, optional) – Number of iterations to build vFWE and cFWE null distributions. Default is 10000.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: ‘z_vthresh’, ‘p_level-voxel’, ‘z_level-voxel’, and ‘logp_level-cluster’.
Notes
This method also adds the following arrays to the CBMAEstimator’s null distributions attribute (
null_distributions_
): ‘fwe_level-voxel_method-montecarlo’ and ‘fwe_level-cluster_method-montecarlo’.See also
nimare.correct.FWECorrector()
The Corrector from which to call this method.
Examples
>>> meta = ALE() >>> result = meta.fit(dset) >>> corrector = FWECorrector(method='montecarlo', voxel_thresh=0.001, 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.