nimare.meta.cbma.ale
.SCALE¶
-
class
SCALE
(voxel_thresh=0.001, n_iters=10000, n_cores=-1, ijk=None, kernel_transformer=<class 'nimare.meta.cbma.kernel.ALEKernel'>, **kwargs)[source]¶ Specific coactivation likelihood estimation.
- Parameters
voxel_thresh (float, optional) – Uncorrected voxel-level threshold. Default: 0.001
n_iters (int, optional) – Number of iterations for correction. Default: 10000
n_cores (int, optional) – Number of processes to use for meta-analysis. If -1, use all available cores. Default: -1
ijk (
str
or (N x 3) array_like) – Tab-delimited file of coordinates from database or numpy array with ijk coordinates. Voxels are rows and i, j, k (meaning matrix-space) values are the three columnns.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.
References
Langner, Robert, et al. “Meta-analytic connectivity modeling revisited: controlling for activation base rates.” NeuroImage 99 (2014): 559-570. https://doi.org/10.1016/j.neuroimage.2014.06.007
-
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.