Common and cluster-specific simultaneous component analysis
View/ Open
Date
2013Author
De Roover, K
Timmerman, ME
Mesquita, B
Ceulemans, E
Type
ArticleLanguage
enMetadata
Show full item recordAbstract
In many fields of research, so-called 'multiblock' data are collected, i.e., data containing multivariate observations that are nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then corresponds to a 'data block'. For such data, it may be interesting to investigate the extent to which the correlation structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by means of component analysis, one may want to explore which components are common across all data blocks and which components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific components. Model estimation and model selection procedures are described and simulation results validate their performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value.
URI
http://hinari-gw.who.int/whalecomwww.ncbi.nlm.nih.gov/whalecom0/pubmed/23667463http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/40755
Citation
De Roover K, Timmerman ME, Mesquita B, Ceulemans E.,Common and cluster-specific simultaneous component analysis.PLoS One. 2013 May 8;8(5):e62280. doi: 10.1371/journal.pone.0062280. Print 2013.Publisher
University of Nairobi, College of Health Sciences,
Collections
- Faculty of Health Sciences (FHS) [10378]