[FIX] fwe docstrings (#868)

* make documentation/docstrings consistent

* modify docstrings

Author: jdkent

examples/02_meta-analyses/04_plot_estimators.py

@@ -112,7 +112,7 @@
 ######################################################################################
 # Note that, to measure significance appropriately with the montecarlo method,
 # you need a lot more than 10 iterations.
-# We recommend 10000 (the default value).
+# We recommend 5000 (the default value).
 mc_meta = ALE(null_method="montecarlo", n_iters=10, n_cores=1)
 mc_results = mc_meta.fit(dset)
 

examples/02_meta-analyses/08_plot_cbma_subtraction_conjunction.py

@@ -138,7 +138,7 @@
 ###############################################################################
 # Subtraction analysis
 # -----------------------------------------------------------------------------
-# Typically, one would use at least 10000 iterations for a subtraction analysis.
+# Typically, one would use at least 5000 iterations for a subtraction analysis.
 # However, we have reduced this to 100 iterations for this example.
 # Similarly here we use a voxel-level z-threshold of 0.01, but in practice one would
 # use a more stringent threshold (e.g., 1.65).

nimare/annotate/gclda.py

@@ -362,8 +362,8 @@ def fit(self, n_iters=5000, loglikely_freq=10):
 
         Parameters
         ----------
-        n_iters : :obj:`int`, optional
-            Number of iterations to run. Default is 10000.
+        n_iters : :obj:`int`, default=5000
+            Number of iterations to run. Default is 5000.
         loglikely_freq : :obj:`int`, optional
             The frequency with which log-likelihood is updated. Default value
             is 1 (log-likelihood is updated every iteration).

nimare/base.py

@@ -101,7 +101,7 @@ def get_params(self, deep=True):
 
         Parameters
         ----------
-        deep : :obj:`bool`, optional
+        deep : :obj:`bool`, default=True
             If True, will return the parameters for this estimator and
             contained subobjects that are estimators.
 
@@ -183,7 +183,7 @@ def load(cls, filename, compressed=True):
         ----------
         filename : :obj:`str`
             Name of file containing object.
-        compressed : :obj:`bool`, optional
+        compressed : :obj:`bool`, default=True
             If True, the file is assumed to be compressed and gzip will be used
             to load it. Otherwise, it will assume that the file is not
             compressed. Default = True.

nimare/cli.py

@@ -61,7 +61,7 @@ def _get_parser():
         dest="n_iters",
         type=int,
         help=("Number of iterations for permutation testing."),
-        default=10000,
+        default=5000,
     )
     macm_parser.add_argument(
         "--v_thr",

nimare/correct.py

@@ -267,11 +267,11 @@ class FWECorrector(Corrector):
         using, use :meth:`inspect`.
     voxel_thresh : :obj:`float`, optional
         Only used if ``method='montecarlo'``. The uncorrected voxel-level threshold to use.
-    n_iters : :obj:`int`, optional
+    n_iters : :obj:`int`, default=5000
         Number of iterations to use for Monte Carlo correction.
         Default varies by Estimator.
         For publication-quality results, 5000 or more iterations are recommended.
-    n_cores : :obj:`int`, optional
+    n_cores : :obj:`int`, default=1
         Number of cores to use for Monte Carlo correction. Default is 1.
     **kwargs
         Keyword arguments to be used by the FWE correction implementation.
@@ -340,12 +340,12 @@ class FDRCorrector(Corrector):
 
     Parameters
     ----------
-    method : :obj:`str`, optional
+    method : :obj:`str`, default='indep'
         The FDR correction to use.
         Either 'indep' (for independent or positively correlated values) or 'negcorr'
         (for general or negatively correlated tests).
         Default is 'indep'.
-    alpha : :obj:`float`, optional
+    alpha : :obj:`float`, default=0.05
         The FDR correction rate to use. Default is 0.05.
 
     Notes

nimare/decode/base.py

@@ -88,7 +88,7 @@ def fit(self, dataset, drop_invalid=True):
         ----------
         dataset : :obj:`~nimare.dataset.Dataset`
             Dataset object to analyze.
-        drop_invalid : :obj:`bool`, optional
+        drop_invalid : :obj:`bool`, default=True
             Whether to automatically ignore any studies without the required data or not.
             Default is True.
 

nimare/estimator.py

@@ -38,7 +38,7 @@ def _collect_inputs(self, dataset, drop_invalid=True):
         Parameters
         ----------
         dataset : :obj:`~nimare.dataset.Dataset`
-        drop_invalid : :obj:`bool`, optional
+        drop_invalid : :obj:`bool`, default=True
             Whether to automatically drop any studies in the Dataset without valid data or not.
             Default is True.
 

nimare/meta/cbma/ale.py

@@ -54,17 +54,17 @@ class ALE(CBMAEstimator):
                                 This method is must slower, and is only slightly more accurate.
         ======================= =================================================================
 
-    n_iters : :obj:`int`, optional
+    n_iters : :obj:`int`, default=5000
         Number of iterations to use to define the null distribution.
         This is only used if ``null_method=="montecarlo"``.
-        Default is 10000.
+        Default is 5000.
     memory : instance of :class:`joblib.Memory`, :obj:`str`, or :class:`pathlib.Path`
         Used to cache the output of a function. By default, no caching is done.
         If a :obj:`str` is given, it is the path to the caching directory.
     memory_level : :obj:`int`, default=0
         Rough estimator of the amount of memory used by caching.
         Higher value means more memory for caching. Zero means no caching.
-    n_cores : :obj:`int`, optional
+    n_cores : :obj:`int`, default=1
         Number of cores to use for parallelization.
         This is only used if ``null_method=="montecarlo"``.
         If <=0, defaults to using all available cores.
@@ -135,7 +135,7 @@ def __init__(
         self,
         kernel_transformer=ALEKernel,
         null_method="approximate",
-        n_iters=None,
+        n_iters=5000,
         memory=Memory(location=None, verbose=0),
         memory_level=0,
         n_cores=1,
@@ -348,15 +348,15 @@ class ALESubtraction(PairwiseCBMAEstimator):
     kernel_transformer : :obj:`~nimare.meta.kernel.KernelTransformer`, optional
         Kernel with which to convolve coordinates from dataset.
         Default is ALEKernel.
-    n_iters : :obj:`int`, optional
-        Default is 10000.
+    n_iters : :obj:`int`, default=5000
+        Default is 5000.
     memory : instance of :class:`joblib.Memory`, :obj:`str`, or :class:`pathlib.Path`
         Used to cache the output of a function. By default, no caching is done.
         If a :obj:`str` is given, it is the path to the caching directory.
     memory_level : :obj:`int`, default=0
         Rough estimator of the amount of memory used by caching.
         Higher value means more memory for caching. Zero means no caching.
-    n_cores : :obj:`int`, optional
+    n_cores : :obj:`int`, default=1
         Number of processes to use for meta-analysis. If -1, use all available cores.
         Default is 1.
 
@@ -652,9 +652,9 @@ class SCALE(CBMAEstimator):
             This parameter was previously incorrectly labeled as "optional" and indicated that
             it supports tab-delimited files, which it does not (yet).
 
-    n_iters : int, optional
-        Number of iterations for statistical inference. Default: 10000
-    n_cores : int, optional
+    n_iters : int, default=5000
+        Number of iterations for statistical inference. Default: 5000
+    n_cores : int, default=1
         Number of processes to use for meta-analysis. If -1, use all available cores.
         Default: 1
     kernel_transformer : :obj:`~nimare.meta.kernel.KernelTransformer`, optional

nimare/meta/cbma/base.py

@@ -402,7 +402,7 @@ def _p_to_summarystat(self, p, null_method=None):
 
         return ss
 
-    def _compute_null_reduced_montecarlo(self, ma_maps, n_iters=10000):
+    def _compute_null_reduced_montecarlo(self, ma_maps, n_iters=5000):
         """Compute uncorrected null distribution using the reduced montecarlo method.
 
         This method is much faster than the full montecarlo approach, but is still slower than the
@@ -615,15 +615,15 @@ def correct_fwe_montecarlo(
         ----------
         result : :obj:`~nimare.results.MetaResult`
             Result object from a CBMA meta-analysis.
-        voxel_thresh : :obj:`float`, optional
+        voxel_thresh : :obj:`float`, default=0.001
             Cluster-defining p-value threshold. Default is 0.001.
-        n_iters : :obj:`int`, optional
+        n_iters : :obj:`int`, default=5000
             Number of iterations to build the voxel-level, cluster-size, and cluster-mass FWE
-            null distributions. Default is 10000.
-        n_cores : :obj:`int`, optional
+            null distributions. Default is 5000.
+        n_cores : :obj:`int`, default=1
             Number of cores to use for parallelization.
             If <=0, defaults to using all available cores. Default is 1.
-        vfwe_only : :obj:`bool`, optional
+        vfwe_only : :obj:`bool`, default=False
             If True, only calculate the voxel-level FWE-corrected maps. Voxel-level correction
             can be performed very quickly if the Estimator's ``null_method`` was "montecarlo".
             Default is False.