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Support liberal mask in IBMA estimators #848

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merged 18 commits into from
Dec 14, 2023
Merged

Support liberal mask in IBMA estimators #848

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45a0b6c
Support liberal mask in IBMA
JulioAPeraza Dec 1, 2023
eccc735
Use numba for calculating the bags
JulioAPeraza Dec 1, 2023
10555c2
add test for _apply_liberal_mask()
JulioAPeraza Dec 1, 2023
67b9ee6
Implement liberal mask across IBMA estimators
JulioAPeraza Dec 1, 2023
c00df4d
Update test_utils.py
JulioAPeraza Dec 1, 2023
7196938
Update ibma.py
JulioAPeraza Dec 1, 2023
75d6601
Update utils.py
JulioAPeraza Dec 1, 2023
c8437aa
Update test_utils.py
JulioAPeraza Dec 1, 2023
bf9254b
Incorporate @tsalo suggestion: Outputs DoF maps
JulioAPeraza Dec 1, 2023
e3737c2
Set data type
JulioAPeraza Dec 2, 2023
56615e5
Update docstring
JulioAPeraza Dec 2, 2023
aa24393
Add doctring. Remove voxels with less that 2 studies from the analsysis
JulioAPeraza Dec 2, 2023
d260837
Update utils.py
JulioAPeraza Dec 2, 2023
8e321e9
Update utils.py
JulioAPeraza Dec 2, 2023
2dd031b
Remove resample option from IBMA examples
JulioAPeraza Dec 2, 2023
ff4f9c3
Apply @jdkent suggestions
JulioAPeraza Dec 13, 2023
7c0e57f
Merge branch 'main' into ibma-masking
JulioAPeraza Dec 13, 2023
6dd2e12
Update ibma.py
JulioAPeraza Dec 13, 2023
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2 changes: 1 addition & 1 deletion examples/02_meta-analyses/02_plot_ibma.py
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Expand Up @@ -86,7 +86,7 @@
# -----------------------------------------------------------------------------
from nimare.meta.ibma import Fishers

meta = Fishers(resample=True)
meta = Fishers()
results = meta.fit(dset)

plot_stat_map(
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3 changes: 1 addition & 2 deletions examples/02_meta-analyses/06_plot_compare_ibma_and_cbma.py
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Expand Up @@ -58,8 +58,7 @@
###############################################################################
# DerSimonian-Laird (IBMA)
# -----------------------------------------------------------------------------
# We must resample the image data to the same MNI template as the Dataset.
meta_ibma = DerSimonianLaird(resample=True)
meta_ibma = DerSimonianLaird()
ibma_results = meta_ibma.fit(dset)
plot_stat_map(
ibma_results.get_map("z"),
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