Projects:LocalStatisticalAnalysisViaPermutationTests - Revision history

L.bompard: /* GLM & MANCOVA */

2011-04-01T20:33:12Z

GLM & MANCOVA

L.bompard: /* GLM & MANCOVA */

2011-04-01T20:25:35Z

GLM & MANCOVA

L.bompard: /* GLM & MANCOVA */

2011-04-01T20:25:26Z

GLM & MANCOVA

L.bompard: /* GLM & MANCOVA */

2011-04-01T19:58:02Z

GLM & MANCOVA

Styner: /* GLM & MANCOVA */

2011-03-30T18:37:44Z

GLM & MANCOVA

Styner: /* GLM & MANCOVA */

2011-03-30T18:26:45Z

GLM & MANCOVA

Styner: /* Key Investigators */

2011-03-30T18:25:37Z

Key Investigators

Marianna: /* Publications */

2010-05-14T14:45:36Z

Publications

Melonakos at 20:24, 11 May 2010

2010-05-11T20:24:30Z

Styner: /* Publications */

2010-05-11T15:44:07Z

Publications

← Older revision		Revision as of 20:33, 1 April 2011
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−	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|400px\|]]	+	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|400px\|shapeAnalysisMANCOVA: quality control for an interaction test]]

	In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.		In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.

← Older revision		Revision as of 20:25, 1 April 2011
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−	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|~~600px~~\|]]	+	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|400px\|]]

	In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.		In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.

← Older revision		Revision as of 20:25, 1 April 2011
Line 24:		Line 24:


−	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|~~300px~~\|]]	+	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|600px\|]]

	In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.		In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.

← Older revision		Revision as of 19:58, 1 April 2011
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	== GLM & MANCOVA ==		== GLM & MANCOVA ==
		+
		+
		+	[[Image:MANCOVA_intercation_test_mrml.jpg\|thumb\|right\|300px\|]]

	In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.		In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.

@@ Line 25: / Line 25: @@
 In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.
 The main MANCOVA metric is the Hotelling Trace, but other standard MANCOVA metrics can be used (such as Ray's Max Lambda). In addition to the permutation based p-values, F-test based significance maps are generated. The MANCOVA based testing framework also contains facilities for z-score testing and correlation testing.
 = Key Investigators =

@@ Line 23: / Line 23: @@
 == GLM & MANCOVA ==
-In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation and correction via the same permutation approach mentioned above.
+In case that correlation statistics are needed, or for the case of unbalanced or not well controlled groups, the novel GLM & MANCOVA statistical framework should be employed. This method first computes a Generalized Linear Model (GLM), followed by a MANCOVA analysis and p-value computation via the same permutation approach mentioned above. Correction for multiple-comparisons is performed via False Discovery Rate.
 The main MANCOVA metric is the Hotelling Trace, but other standard MANCOVA metrics can be used (such as Ray's Max Lambda). In addition to the permutation based p-values, F-test based significance maps are generated. The MANCOVA based testing framework also contains facilities for z-score testing and correlation testing.

@@ Line 28: / Line 28: @@
 = Key Investigators =
-* UNC Algorithms: Martin Styner, Ipek Oguz, Marc Niethammer, Marc Macenko, Beatriz Paniagua, Christine Shun Xu, Hongtu Zhu, Corentin Hamel
+* UNC Algorithms: Martin Styner, Ipek Oguz, Marc Niethammer, Marc Macenko, Beatriz Paniagua, Christine Shun Xu, Hongtu Zhu, Corentin Hamel, Lucile Bompard
 * Utah Algorithms: Guido Gerig

@@ Line 32: / Line 32: @@
 = Publications =
 * [http://www.na-mic.org/publications/pages/display?search=LocalStatisticalAnalysisViaPermutationTests NA-MIC Publications Database on Local Statistical Analysis via Permutation Tests]
 = Links =

@@ Line 34: / Line 34: @@
 ''In Print''
-* [http://www.na-mic.org/publications/pages/display?search=LocalStatisticalAnalysisViaPermutationTests&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database]
+* [http://www.na-mic.org/publications/pages/display?search=LocalStatisticalAnalysisViaPermutationTests&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Local Statistical Analysis via Permutation Tests]
 = Links =