Non-linear principal components analysis: an alternative method for finding patterns in environmental data

The main purpose of this article is to gain an insight into the relationships between variables describing the environmental conditions of the Far Northern section of the Great Barrier Reef, Australia, Several of the variables describing these conditions had different measurement levels and often they had non-linear relationships. Using non-linear principal component analysis, it was possible to acquire an insight into these relationships. Furthermore. three geographical areas with unique environmental characteristics could be identified. Copyright (c) 2005 John Wiley & Sons, Ltd.

Measuring community strength and social capital

Five case study communities in both metropolitan and regional urban locations in Australia are used as test sites to develop measures of 'community strength' on four domains: Natural Capital; Produced Economic Capital; Human Capital; and Social and Institutional Capital. The paper focuses on the fourth domain. Sample surveys of households in the five case study communities used a survey instrument with scaled items to measure four aspects of social capital - formal norms, informal norms, formal structures and informal structures - that embrace the concepts of trust, reciprocity, bonds, bridges, links and networks in the interaction of individuals with their community inherent in the notion social capital. Exploratory principal components analysis is used to identify factors that measure those aspects of social and institutional capital, while a confirmatory analysis based on Cronbach's alpha explores the robustness of the measures. Four primary scales and 15 subscales are identified when defining the domain of social and institutional capital. Further analysis reveals that two measures - anomie, and perceived quality of life and wellbeing - relate to certain primary scales of social capital.

Cluster analysis of high-dimensional data: A case study

Normal mixture models are often used to cluster continuous data. However, conventional approaches for fitting these models will have problems in producing nonsingular estimates of the component-covariance matrices when the dimension of the observations is large relative to the number of observations. In this case, methods such as principal components analysis (PCA) and the mixture of factor analyzers model can be adopted to avoid these estimation problems. We examine these approaches applied to the Cabernet wine data set of Ashenfelter (1999), considering the clustering of both the wines and the judges, and comparing our results with another analysis. The mixture of factor analyzers model proves particularly effective in clustering the wines, accurately classifying many of the wines by location.

Determining the effective dimensionality of the genetic variance-covariance matrix

Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.

Internationale Konjunkturverbunde

After conceptual clarification of international business cycle and a review of the literature, a new indicator is proposed. This indicator refers to two time series only and allows for an internationally comparable quantification of a country's position in the business cycle. We then calculate times series of this indicator for 30 countries from 1970-2000. After some plausibility checks, we refer to these series to test a number of hypotheses. Cross correlations reveal a high degree of interconnectedness. Moreover, the number of highly positive correlations has increased over time, whereas the number of low and moderate correlations has decreased. A principal components analysis yields a first component that can be interpreted as the world business cycle. The further components suggest the existence of a Scandinavian-Anglo-Saxon business cycle as well as of another, smaller group of Anglo-Saxon countries that move together. This finding is replicated by a hierarchical cluster analysis, which in addition suggests a closely integrated group of non-Scandinavian and non-English speaking European countries plus Japan and Israel. Furthermore, there is indication for some, albeit weak business cycle integration in Southeast Asia and in South America. The international business cycle is thus found to have a hierarchical structure.

Measuring outcomes of a communication program for older people with hearing impairment using the International Outcome Inventory

The main objective of this study was to describe the outcomes of a communication education program for older people with hearing impairment using the International Outcome Inventory - Alternative Interventions (IOI-AI) and the version for significant others (IOI-AI-SO). Ninety-six people aged 58 to 94 years participated in an interactive group education program for two hours per week for five weeks. The IOI-AI was administered at one to two weeks after the last educational session and 29 significant others also completed the IOI-Al-SO at this time. Overall, positive results were obtained using both questionnaires, and satisfaction with the program was particularly high. Findings also compared favourably to reports of outcomes for other audiological interventions (i.e., another communication training program and hearing aid fitting). Principal components analysis of the IOI-AI revealed a somewhat different factor structure than the original IOI-HA. The two versions of the 101 applied in this study are recommended as simple and effective measures of the outcomes of alternative interventions.

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.

Substantial changes in the genetic basis of tadpole morphology of Rana lessonae in the presence of predators

Predator-induced morphological plasticity is a model system for investigating phenotypic plasticity in an ecological context. We investigated the genetic basis of the predator-induced plasticity in Rana lessonae by determining the pattern of genetic covariation of three morphological traits that were found to be induced in a predatory environment. Body size decreased and tail dimensions increased when reared in the presence of preying dragonfly larvae. Genetic variance in body size increased by almost an order of magnitude in the predator environment, and the first genetic principal component was found to be highly significantly different between the two environments. The across environment genetic correlation for body size was significantly below 1 indicating that different genes contributed to this trait in the two environments. Body size may therefore be able to respond to selection independently in the two environments to some extent.

Validation of the Quality of Life Inventory for patients with anxiety and depression

Background: In 1992, Frisch et al (Psychol Assess. 1992;4:92- 10 1) developed the Quality of Life Inventory (QOLI) to measure the concept of quality of life (QOL) because it has long been thought to be related to both physical and emotional well-being. However, the psychometric properties of the QOLI in clinical populations are still in debate. The present study examined the factor structure of QOLI and reported its validity and reliability in a clinical sample. Method: Two hundred seventeen patients with anxiety and depressive disorders completed the QOLI and additional questionnaires measuring symptoms (Zung Self-rating Depression Scale, Beck Anxiety Inventory, Fear Questionnaire, Depression Anxiety Stress Scale-Stress) and subjective well-being (Satisfaction With Life Scale) were also used. Results: Exploratory factor analysis via the principal components method, with oblique rotation, revealed a 2-factor structure that accounted for 42.73% of the total variance, and a subsequent confirmatory factor analysis suggested a moderate fit of the data to this model. The 2 factors appeared to describe self-oriented QOL and externally oriented QOL. The Cronbach alpha coefficients were 0.85 for the overall QOLI score, 0.81 for the first factor, and 0.75 for the second factor. Conclusion: Consistent evidence was also found to support the concurrent, discriminant, predictive, and criterion-related validity of the QOLI. (c) 2006 Elsevier Inc. All rights reserved.

Geochemical changes recorded in Lynch's Crater, Northeastern Australia, over the past 50 ka

There are many geochemical reconstructions of environmental change in the mid and high latitudes but relatively few in the tropical latitudes, despite their considerable potential for reconstructing environmental processes that cannot be identified using more traditional proxies. Here we present one reconstruction of environmental change for the tropics. This reconstruction covers the past 50 ka using a suite of geochemical data from the high-resolution sequence of Lynch's Crater in northeast Queensland, Australia, a region highly sensitive to El Nino-Southern Oscillation (ENSO) activity. The 23 major oxides and trace elements measured Could be summarised by extracting three axes using principal components analysis (accounting for 72% of the variability). The data indicate that the greatest variability in the geochemical data accounted for erosional activity within the catchment that was associated with past changes in the frequency of ENSO activity (though this was less sensitive during wetter periods, probably as a result of buffering by high vegetation cover). The remaining variability was largely explained by elements that form complexes with organic compounds (e.g., humic acids) and those that are important nutrients for specific vegetation types (and therefore a measure of vegetation distribution). For more detailed reconstructions, further work is required to disentangle the complex controls of clements within sedimentary sequences. (c) 2005 Elsevier B.V. All rights reserved.

The stability of P in coral reef fishes

The constancy of phenotypic variation and covariation is an assumption that underlies most recent investigations of past selective regimes and attempts to predict future responses to selection. Few studies have tested this assumption of constancy despite good reasons to expect that the pattern of phenotypic variation and covariation may vary in space and time. We compared phenotypic variance-covariance matrices (P) estimated for Populations of six species of distantly related coral reef fishes sampled at two locations on Australia's Great Barrier Reef separated by more than 1000 km. The intraspecific similarity between these matrices was estimated using two methods: matrix correlation and common principal component analysis. Although there was no evidence of equality between pairs of P, both statistical approaches indicated a high degree of similarity in morphology between the two populations for each species. In general, the hierarchical decomposition of the variance-covariance structure of these populations indicated that all principal components of phenotypic variance-covariance were shared but that they differed in the degree of variation associated with each of these components. The consistency of this pattern is remarkable given the diversity of morphologies and life histories encompassed by these species. Although some phenotypic instability was indicated, these results were consistent with a generally conserved pattern of multivariate selection between populations.

Symptom prevalence and clustering of symptoms in people living with chronic hepatitis C infection

Quality of life has been shown to be poor among people living with chronic hepatitis C However, it is not clear how this relates to the presence of symptoms and their severity. The aim of this study was to describe the typology of a broad array of symptoms that were attributed to hepatitis C virus (HCV) infection. Phase I used qualitative methods to identify symptoms. In Phase 2, 188 treatment-naive people living with HCV participated in a quantitative survey. The most prevalent symptom was physical tiredness (86%) followed by irritability (75%), depression (70%), mental tiredness (70%), and abdominal pain (68%). Temporal clustering of symptoms was reported in 62% of participants. Principal components analysis identified four symptom clusters: neuropsychiatric (mental tiredness, poor concentration, forgetfulness, depression, irritability, physical tiredness, and sleep problems); gastrointestinal (day sweats, nausea, food intolerance, night sweats, abdominal pain, poor appetite, and diarrhea); algesic (joint pain, muscle pain, and general body pain); and dysesthetic (noise sensitivity, light sensitivity, skin. problems, and headaches). These data demonstrate that symptoms are prevalent in treatment-naive people with HCV and support the hypothesis that symptom clustering occurs.

Measuring community strength and social capital

In 2001/02 five case study communities in both metropolitan and regional urban locations in Australia were chosen as test sites to develop measures of community strength on four domains: natural capital; produced economic capital; human capital; and social and institutional capital. Secondary data sources were used to develop measures on the first three domains. For the fourth domain social and institutional capital primary data collection was undertaken through sample surveys of households. A structured approach was devised. This involved developing a survey instrument using scaled items relating to four elements: formal norms; informal norms; formal structures; and informal structures which embrace the concepts of trust, reciprocity, bonds, bridges, links and networks in the interaction of individuals with their community inherent in the notion social capital. Exploratory principal components analysis was used to identify factors that measure those aspects of social and institutional capital, with confirmatory analysis conducted using Cronbach's Alpha. This enabled the construction of four primary scales and 15 sub-scales as a tool for measuring social and institutional capital. Further analyses reveals that two measures anomie and perceived quality of life and wellbeing relate to certain primary scales of social capital.