The Success of Restricted Ordination Methods in Data Analysis with Variables at Different Scale Levels
Abstract
Events in nature occur with the effect of many interrelated variables, either separately or together. It is important to introduce and use the methods used for the analysis of data sets at different scale levels with linear and nonlinear relationship structure between variables. Redundacy Analysis and Canonical Correspondence Analysis are among the methods used in the analysis of such data. Aforementioned techniques are generally carried out by ecologists and there are limited studies in the field of health. In the study, the application of the methods was performed with a data set in the field of Cardiology including variables at different scale levels and their performances were compared. Determination Coefficient (R2) and MAPE (Mean Absolute Percentage Error) value were calculated as performance criteria. According to the results, it was seen that CCA and RDA, which analyze the relationship structures between variables in different scale types (cardiological data set), explain the variation sufficiently. Also, it was emphasized that both methods classify well with low MAPE value (less than 10%) and perform ordination diagram. In addition, it has been observed that restricted ordination diagram models give satisfactory results in determining the relationships between coronary heart disease data and so that they can be used in the field of health too.
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- Witt, S. F. and Witt, C. A. (1992). Modelling and forecasting demand in tourism. London: Academic Press.
Abstract
Doğadaki olaylar, birbiriyle ilişkili birçok değişkenin ayrı ayrı veya birlikte etkisiyle meydana gelir. Değişkenler arası doğrusal ve doğrusal olmayan ilişki yapısı ile farklı ölçek düzeylerindeki veri setlerinin analizi için kullanılan yöntemlerin tanıtılması ve kullanılması önemlidir. Bu tür verilerin analizinde kullanılan yöntemler arasında Gereksizlik Analizi ve Kanonik Uyum Analizi yer almaktadır. Yukarıda belirtilen tekniklerle ilgili çalışmaların genellikle ekolojistler tarafından yapıldığı ve sağlık alanında sınırlı sayıda çalışma olduğu görülmüştür. Bu nedenle çalışmada, belirtilen yöntemlerin uygulamaları farklı ölçek düzeylerindeki değişkenleri içeren bir Kardiyoloji veri seti üzerinde gerçekleştirilmiş ve performansları karşılaştırılmıştır. Performans kriteri olarak Belirleme Katsayısı (R2) ve MAPE (Ortalama Mutlak Yüzde Hata) değeri hesaplandı. Elde edilen sonuçlara göre, farklı ölçek türlerindeki (kardiyolojik veri seti) değişkenler arasındaki ilişki yapılarını inceleyen CCA ve RDA'nın, varyasyonu yeterince açıkladığı görülmüştür. Ayrıca her iki yöntemin de düşük MAPE değeri (% 10'dan az) ile iyi sınıflandırıldığı ve koordinasyon diyagramı gerçekleştirdiği vurgulanmıştır. Ayrıca, kısıtlı koordinasyon diyagram modellerinin koroner kalp hastalığı verileri arasındaki ilişkilerin belirlenmesinde tatmin edici sonuçlar verdiği ve benzeri sağlık alanlarında da kullanılabilirliği görülmüştür.
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- Buttigieg, P. L. and Ramette, A. (2014). A Guide to Statistical Analysis in Microbial Ecology: a community-focused. living review of multivariate data analyses. FEMS Microbiol Ecol. 90(1), 543–50.
- Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(1), 453–67.
- Israels, AZ. Redundancy Analysis for Various Types of Variables. Statistica Applicata, 4(4): 531-42, (1992).
- Jongman, R. H. G., Ter Braak, C. J. F. and Van Tongeren, O. R. (1987). Data Analysis in Community and Landscape Ecology. Pudoc. Wageningen.
- Lambert, Z., Wildt, A. R. and Durand, R.M. (1988). Redundancy analysis: An alternative to canonical correlation and multivariate multiple regression in exploring interset associations. Psychological Bull. 104(2), 282-9.
- Legendre, P. and Legendre, L. (1988) Numerical Ecolojy. Second English Edition. Elseiver Scıence B.V. Amsterdam. Netherlands. ISBN 978-0444892508.
- Lewis, C.D. (1982). Industrial and Business Forecasting Methods. Londra: Butterworths Publishing.
- Mardia, K.V. Kent, J.T. and Bibby, J. M. (1979). Multivariate analysis. Academic Press. London. England.
- Mc Cune, B. and Grace, J. B. (2002). Analysis of Ecological Communities. MjM Software Design. Gleneden Beach. Oregon.
- Muller, K. E. (1981). Relationships Between Redundancy Analysis, Canonical Correlation and Multivariate Regression. Psychometrica 46(2), 139-42.
- O’Connell, M. T., Robert, C. C. and Chrıstopher, S. S. (2004). Fish Assemblage Stability Over Fifty Years in the Lake Pontchartrain Estuary; Comparisons Among Habitats Using Canonical Correspondence Analysis. Estuaries 5(27), 807–17.
- Oksanen, J. (2004). Multivariate Analysis in Ecology (Lecture notes). Department of Biology. University of Oulu.
- Palmer, M. W. (1993). Putting things in even better order: the advantages of canonical correspondence analysis. Ecol. 8(74), 2215-30.
- Palmer, M. W. (2017). Access address: http:// ordination.okstate.edu/overview.htm# Redundancy_ Analysis. Date of access: 18/05/2018.
- Ramette, A. (2007). Multivariate analyses in microbial ecology. FEMS Microbiol Ecol. 62(2), 142–60.
- Saporta, G. (1990). Probabilites. analyses de don&es et statistiques. Editions Technip. Paris. 492-93.
- Ter Braak, C. J. F. (1986). Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. ESA. 5(67), 1167-79.
- Ter Braak, C. J. F. (1987). The Analysis of Vegetation-Environment Relationships by Canonical Correspondence Analysis. Vegetatio 64, 69-77.
- Ter Braak, C. J. F. and Prentice, I.C. (1988). A theory of gradient analysis. Adv Ecol Res. 18, 271–317.
- Ter Braak, C. J. F. (1994). Canonical community ordination. Part I: basic theory and linear methods. Ecoscience 1, 127–40.
- Tso, M. K. S. (1981). Reduced-rank regression and canonical analysis. J Roy Statist Soc. 43(B), 183–89.
- Witt, S. F. and Witt, C. A. (1992). Modelling and forecasting demand in tourism. London: Academic Press.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Publication Date | March 31, 2021 |
| Published in Issue | Year 2021 Volume: 14 Issue: 1 |