Multivariate Statistical Methods

Manly, Bryan F. J.

doi:10.1201/b16974

Cited by 723 publications

(677 citation statements)

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“…16,21 In PCA, the variance of PC1 is maximized, and the sum of the squared loadings ( P a 2 1j ) is constrained to one. 16 The magnitude and direction of the loadings or eigenvectors are used to interpret the orthogonal components. Readers interested in the details of PCA are referred to the references listed above as well as Tabachnick and Fidell 22 and Stevens.…”

Section: Discussionmentioning

confidence: 99%

“…One approach to dealing with many inter correlated study variables is to use principal components analysis (PCA). 16 PCA is a data reduction method that can help identify latent factors or principal components (PC) that underlie multivariate BP data by simultaneously incorporating information from each BP measurement. This process also helps to reduce redundancy among BP data because the resulting components are orthogonal or independent from one another.…”

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Quantitative genetic analysis of blood pressure reactivity to orthostatic tilt using principal components analysis

et al. 2006

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Quantitative genetic analysis of blood pressure reactivity to orthostatic tilt using principal components analysis

et al. 2006

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“…PCs thereby constitute a new orthonormal coordinate system whose unity vectors are aligned with the directions of largest variance. Although the number of PCs is equal to the number of variables often only the first few components are needed to capture most of the variance in a data set (see also Manly, 2004). …”

Section: Pcamentioning

confidence: 99%

Inter-joint coupling and joint angle synergies of human catching movements

Bockemühl

Troje

Dürr

2010

Human Movement Science

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A central question in motor control is how the central nervous system (CNS) deals with redundant degrees of freedom (DoFs) inherent in the musculoskeletal system. One way to simplify control of a redundant system is to combine several DoFs into synergies. In reaching movements of the human arm, redundancy occurs at the kinematic level because there are an unlimited number of arm postures for each position of the hand. Redundancy also occurs at the level of muscle forces because each arm posture can be maintained by a set of muscle activation patterns. Both postural and force-related motor synergies may contribute to simplify the control problem. The present study analyzes the kinematic complexity of natural, unrestrained human arm movements, and detects the amount of kinematic synergy in a vast variety of arm postures. We have measured interjoint coupling of the human arm and shoulder girdle during fast, unrestrained and untrained catching movements. Participants were asked to catch a ball launched towards them on 16 different trajectories. These had to be reached from two different initial positions. Movement of the right arm was recorded using optical motion capture and was transformed into 10 joint angle time courses, corresponding to 3 DoFs of the shoulder girdle and 7 of the arm. The resulting time series of the arm postures were analyzed by Principal Components Analysis (PCA). We found that the first three principal components (PCs) always captured more than 97% of the variance. Furthermore, subspaces spanned by PC sets associated with different catching positions varied smoothly across the arm's workspace. When we pooled complete sets of movements, three PCs, the theoretical minimum for reaching in 3D space, were sufficient to explain 80% of the data's variance. We assumed that the linearly correlated DoFs of each significant PC represent cardinal joint angle synergies, and showed that catching movements towards a multitude of targets in the arm's workspace can be generated efficiently by linear combinations of three of such synergies. The contribution of each synergy changed during a single catching movement and often varied systematically with target location. We conclude that unrestrained, one-handed catching movements are dominated by strong kinematic couplings between the joints that reduce the kinematic complexity of the human arm and shoulder girdle to three non-redundant DoFs. ACCEPTED MANUSCRIPT4

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“…Statistical analysis Goat farms were grouped based on a K mean cluster analysis (Manly, 2004) using the weighted OLPI as a classification variable. Later, the indicators of the conglomerates were analysed using a single factor or one-way ANOVA.…”

Section: Methodsmentioning

confidence: 99%

Evaluating mountain goat dairy systems for conversion to the organic model, using a multicriteria method

Guerrero

Nahed

Ruiz

et al. 2012

Animal

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Organic farming conserves natural resources, promotes biodiversity, guarantees animal welfare and obtains healthy products from raw materials through natural processes. In order to evaluate possibilities of increasing organic animal production, this study proposes a farm-scale multicriteria method for assessing the conversion of dairy goat systems to the organic model. In addition, a case study in the Northern Sierra of Seville, southern Spain, is analysed. A consensus of expert opinions and a field survey are used to validate a list of potential indicators and issues for assessing the conversion, which consider not only the European Community regulations for organic livestock farming, but also agroecological principles. As a result, the method includes 56 variables integrated in nine indicators: Nutritional management, Sustainable pasture management, Soil fertility and contamination, Weed and pest control, Disease prevention, Breeds and reproduction, Animal welfare, Food safety and Marketing and management. The nine indicators are finally integrated in a global index named OLPI (Organic Livestock Proximity Index). Application of the method to a case study with 24 goat farms reveals an OLPI value of 46.5% for dairy goat farms located in mountain areas of southern Spain. The aspects that differ most from the agroecological model include soil management, animal nutrition and product marketing. Results of the case study indicate that the proposed method is easy to implement and is useful for quantifying the approximation of conventional farms to an organic model.

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Multivariate Statistical Methods

Cited by 723 publications

References 1 publication

Quantitative genetic analysis of blood pressure reactivity to orthostatic tilt using principal components analysis

Quantitative genetic analysis of blood pressure reactivity to orthostatic tilt using principal components analysis

Inter-joint coupling and joint angle synergies of human catching movements

Evaluating mountain goat dairy systems for conversion to the organic model, using a multicriteria method

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