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[DOC] Improve documentation of computefeats2 #458

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merged 6 commits into from
Jan 17, 2020
Merged

[DOC] Improve documentation of computefeats2 #458

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2356722
adding more doc to computefeats2
CesarCaballeroGaudes Nov 13, 2019
948a08d
Added the default option add_const=False for user-friendliness
CesarCaballeroGaudes Nov 25, 2019
62082e9
changes following PR review & linter check
CesarCaballeroGaudes Nov 25, 2019
69fe33d
Update tedana/stats.py
CesarCaballeroGaudes Dec 13, 2019
95ade44
Update tedana/stats.py
CesarCaballeroGaudes Jan 13, 2020
4c936a6
Update tedana/stats.py
CesarCaballeroGaudes Jan 14, 2020
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2 changes: 1 addition & 1 deletion tedana/metrics/kundu_fit.py
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Expand Up @@ -107,7 +107,7 @@ def dependence_metrics(catd, tsoc, mmix, t2s, tes, ref_img,
WTS = computefeats2(tsoc, mmixN, mask=None, normalize=False)

# compute PSC dataset - shouldn't have to refit data
tsoc_B = get_coeffs(tsoc_dm, mmix, mask=None)
tsoc_B = get_coeffs(tsoc_dm, mmix, mask=None, add_const=False)
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del tsoc_dm
tsoc_Babs = np.abs(tsoc_B)
PSC = tsoc_B / tsoc.mean(axis=-1, keepdims=True) * 100
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8 changes: 7 additions & 1 deletion tedana/stats.py
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Expand Up @@ -74,10 +74,14 @@ def computefeats2(data, mmix, mask=None, normalize=True):
# demean masked data
if mask is not None:
data = data[mask, ...]
# normalize data (minus mean and divide by std) in the last dimension
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# so that least-squares estimates represent "approximate" correlation values (data_R)
# assuming mixing matrix (mmix) values are also normalized
data_vn = stats.zscore(data, axis=-1)

# get betas of `data`~`mmix` and limit to range [-0.999, 0.999]
data_R = get_coeffs(data_vn, mmix, mask=None)
# Avoid abs(data_R) => 1, otherwise Fisher's transform will return Inf or -Inf
data_R[data_R < -0.999] = -0.999
data_R[data_R > 0.999] = 0.999

Expand All @@ -86,9 +90,11 @@ def computefeats2(data, mmix, mask=None, normalize=True):
if data_Z.ndim == 1:
data_Z = np.atleast_2d(data_Z).T

# normalize data
# normalize data (only division by std)
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if normalize:
# minus mean and divided by std
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data_Zm = stats.zscore(data_Z, axis=0)
# adding back the mean
data_Z = data_Zm + (data_Z.mean(axis=0, keepdims=True) /
data_Z.std(axis=0, keepdims=True))

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