A new method for constructing networks from binary data
Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian...
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| Veröffentlicht in: | Scientific reports 2014-08, Vol.4 (1), p.5918-5918, Article 5918 |
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| creator | van Borkulo, Claudia D. Borsboom, Denny Epskamp, Sacha Blanken, Tessa F. Boschloo, Lynn Schoevers, Robert A. Waldorp, Lourens J. |
| description | Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed. |
| doi_str_mv | 10.1038/srep05918 |
| format | Article |
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A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. 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All rights reserved</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c504t-b8e2c576907e3c8ae8f0ed86dee31dc76bc7ea6aaa87866f8b655df7c5f51aa93</citedby><cites>FETCH-LOGICAL-c504t-b8e2c576907e3c8ae8f0ed86dee31dc76bc7ea6aaa87866f8b655df7c5f51aa93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118196/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118196/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,41120,42189,51576,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25082149$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>van Borkulo, Claudia D.</creatorcontrib><creatorcontrib>Borsboom, Denny</creatorcontrib><creatorcontrib>Epskamp, Sacha</creatorcontrib><creatorcontrib>Blanken, Tessa F.</creatorcontrib><creatorcontrib>Boschloo, Lynn</creatorcontrib><creatorcontrib>Schoevers, Robert A.</creatorcontrib><creatorcontrib>Waldorp, Lourens J.</creatorcontrib><title>A new method for constructing networks from binary data</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. 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Possible extensions of the model are discussed.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>25082149</pmid><doi>10.1038/srep05918</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 631/477 639/705 692/1807 Algorithms Anxiety Approximation Case-Control Studies Computer applications Computer Simulation Data processing Depression - diagnosis Depression - psychology Humanities and Social Sciences Humans Mental depression Methods Models, Theoretical multidisciplinary Psychology Psychopathology Regression analysis Science Software Validation studies Variables |
| title | A new method for constructing networks from binary data |
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