Assessing food dietary intakes in Japanese-Brazilians using factor analysis

We describe the use of factor analysis for assessing food habits in Japanese-Brazilians. Dietary data from 1,283 participants of a cross-sectional study were used. Besides statistical criteria, we also used the conceptual meaning of identified profiles to obtain scores for dietary patterns (Japanese or Western profile). Paired Student t test, linear regression and Poisson models were used to verify the existence of relationship between these scores and generation, body mass index (BMI), waist circumference and presence of metabolic syndrome, respectively. First generation subjects had higher mean Japanese profile scores and lower Western profile scores than those of second generation. The Western dietary pattern was associated with BMI (p = 0.001), waist circumference (p = 0.023) and metabolic syndrome (p < 0.05). We concluded that these scores were able to discriminate subjects who maintained their traditional Japanese lifestyle or otherwise, and that the incorporation of a Western lifestyle is associated to high values of BMI, waist circumference and presence of metabolic syndrome.

Factor analysis in the stock market: An application to statistical arbitrage

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Statistical shape analysis via principal factor analysis

Statistical shape analysis techniques commonly employed in the medical imaging community, such as active shape models or active appearance models, rely on principal component analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose principal factor analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficient technique that performs dimensionality reduction (as opposed to independent component analysis, ICA). The key difference between PFA and PCA is that PFA models covariance between variables, rather than the total variance in the data. The added value of PFA is illustrated on 2D landmark data of corpora callosa outlines. Then, a study of the 3D shape variability of the human left femur is performed. Finally, we report results on vector-valued 3D deformation fields resulting from non-rigid registration of ventricles in MRI of the brain.

Statistical analysis of the goodness of classical factor analysis regression (CFAR) /

Cover title.

An SPSS R-Menu for Ordinal Factor Analysis

Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calcula- tions. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It o ers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper o ers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Orthogonal higher order structure and confirmatory factor analysis of the French Wechsler Adult Intelligence Scale (WAIS-III).

According to the most widely accepted Cattell-Horn-Carroll (CHC) model of intelligence measurement, each subtest score of the Wechsler Intelligence Scale for Adults (3rd ed.; WAIS-III) should reflect both 1st- and 2nd-order factors (i.e., 4 or 5 broad abilities and 1 general factor). To disentangle the contribution of each factor, we applied a Schmid-Leiman orthogonalization transformation (SLT) to the standardization data published in the French technical manual for the WAIS-III. Results showed that the general factor accounted for 63% of the common variance and that the specific contributions of the 1st-order factors were weak (4.7%-15.9%). We also addressed this issue by using confirmatory factor analysis. Results indicated that the bifactor model (with 1st-order group and general factors) better fit the data than did the traditional higher order structure. Models based on the CHC framework were also tested. Results indicated that a higher order CHC model showed a better fit than did the classical 4-factor model; however, the WAIS bifactor structure was the most adequate. We recommend that users do not discount the Full Scale IQ when interpreting the index scores of the WAIS-III because the general factor accounts for the bulk of the common variance in the French WAIS-III. The 4 index scores cannot be considered to reflect only broad ability because they include a strong contribution of the general factor.

A Factor analysis of hidrochemical composition of Llobregat river basin

Hydrogeological research usually includes some statistical studies devised to elucidate mean background state, characterise relationships among different hydrochemical parameters, and show the influence of human activities. These goals are achieved either by means of a statistical approach or by mixing modelsbetween end-members. Compositional data analysis has proved to be effective with the first approach, but there is no commonly accepted solution to the end-member problem in a compositional framework.We present here a possible solution based on factor analysis of compositions illustrated with a case study.We find two factors on the compositional bi-plot fitting two non-centered orthogonal axes to the most representative variables. Each one of these axes defines a subcomposition, grouping those variables thatlay nearest to it. With each subcomposition a log-contrast is computed and rewritten as an equilibrium equation. These two factors can be interpreted as the isometric log-ratio coordinates (ilr) of three hiddencomponents, that can be plotted in a ternary diagram. These hidden components might be interpreted as end-members.We have analysed 14 molarities in 31 sampling stations all along the Llobregat River and its tributaries, with a monthly measure during two years. We have obtained a bi-plot with a 57% of explained totalvariance, from which we have extracted two factors: factor G, reflecting geological background enhanced by potash mining; and factor A, essentially controlled by urban and/or farming wastewater. Graphicalrepresentation of these two factors allows us to identify three extreme samples, corresponding to pristine waters, potash mining influence and urban sewage influence. To confirm this, we have available analysisof diffused and widespread point sources identified in the area: springs, potash mining lixiviates, sewage, and fertilisers. Each one of these sources shows a clear link with one of the extreme samples, exceptfertilisers due to the heterogeneity of their composition.This approach is a useful tool to distinguish end-members, and characterise them, an issue generally difficult to solve. It is worth note that the end-member composition cannot be fully estimated but only characterised through log-ratio relationships among components. Moreover, the influence of each endmember in a given sample must be evaluated in relative terms of the other samples. These limitations areintrinsic to the relative nature of compositional data

A Factor analysis of hidrochemical composition of Llobregat river basin

Hydrogeological research usually includes some statistical studies devised to elucidate mean background state, characterise relationships among different hydrochemical parameters, and show the influence of human activities. These goals are achieved either by means of a statistical approach or by mixing models between end-members. Compositional data analysis has proved to be effective with the first approach, but there is no commonly accepted solution to the end-member problem in a compositional framework. We present here a possible solution based on factor analysis of compositions illustrated with a case study. We find two factors on the compositional bi-plot fitting two non-centered orthogonal axes to the most representative variables. Each one of these axes defines a subcomposition, grouping those variables that lay nearest to it. With each subcomposition a log-contrast is computed and rewritten as an equilibrium equation. These two factors can be interpreted as the isometric log-ratio coordinates (ilr) of three hidden components, that can be plotted in a ternary diagram. These hidden components might be interpreted as end-members. We have analysed 14 molarities in 31 sampling stations all along the Llobregat River and its tributaries, with a monthly measure during two years. We have obtained a bi-plot with a 57% of explained total variance, from which we have extracted two factors: factor G, reflecting geological background enhanced by potash mining; and factor A, essentially controlled by urban and/or farming wastewater. Graphical representation of these two factors allows us to identify three extreme samples, corresponding to pristine waters, potash mining influence and urban sewage influence. To confirm this, we have available analysis of diffused and widespread point sources identified in the area: springs, potash mining lixiviates, sewage, and fertilisers. Each one of these sources shows a clear link with one of the extreme samples, except fertilisers due to the heterogeneity of their composition. This approach is a useful tool to distinguish end-members, and characterise them, an issue generally difficult to solve. It is worth note that the end-member composition cannot be fully estimated but only characterised through log-ratio relationships among components. Moreover, the influence of each endmember in a given sample must be evaluated in relative terms of the other samples. These limitations are intrinsic to the relative nature of compositional data

Robust Factor Analysis for Compositional Data

Factor analysis as frequent technique for multivariate data inspection is widely used also for compositional data analysis. The usual way is to use a centered logratio (clr) transformation to obtain the random vector y of dimension D. The factor model is then y = Λf + e (1) with the factors f of dimension k < D, the error term e, and the loadings matrix Λ. Using the usual model assumptions (see, e.g., Basilevsky, 1994), the factor analysis model (1) can be written as Cov(y) = ΛΛT + ψ (2) where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as the loadings matrix Λ are estimated from an estimation of Cov(y). Given observed clr transformed data Y as realizations of the random vector y. Outliers or deviations from the idealized model assumptions of factor analysis can severely effect the parameter estimation. As a way out, robust estimation of the covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), see Pison et al. (2003). Well known robust covariance estimators with good statistical properties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), rely on a full-rank data matrix Y which is not the case for clr transformed data (see, e.g., Aitchison, 1986). The isometric logratio (ilr) transformation (Egozcue et al., 2003) solves this singularity problem. The data matrix Y is transformed to a matrix Z by using an orthonormal basis of lower dimension. Using the ilr transformed data, a robust covariance matrix C(Z) can be estimated. The result can be back-transformed to the clr space by C(Y ) = V C(Z)V T where the matrix V with orthonormal columns comes from the relation between the clr and the ilr transformation. Now the parameters in the model (2) can be estimated (Basilevsky, 1994) and the results have a direct interpretation since the links to the original variables are still preserved. The above procedure will be applied to data from geochemistry. Our special interest is on comparing the results with those of Reimann et al. (2002) for the Kola project data

Prevalence and risk-factor analysis of Shiga toxigenic Escherichia coli in faecal samples of organically and conventionally farmed dairy cattle

Cattle are a natural reservoir for Shiga toxigenic Escherichia coli (STEC), however, no data are available on the prevalence and their possible association with organic or conventional farming practices. We have therefore studied the prevalence of STEC and specifically O157:H7 in Swiss dairy cattle by collecting faeces from approximately 500 cows from 60 farms with organic production (OP) and 60 farms with integrated (conventional) production (IP). IP farms were matched to OP farms and were comparable in terms of community, agricultural zone, and number of cows per farm. E. coli were grown overnight in an enrichment medium, followed by DNA isolation and PCR analysis using specific TaqMan assays. STEC were detected in all farms and O157:H7 were present in 25% of OP farms and 17% of IP farms. STEC were detected in 58% and O157:H7 were evidenced in 4.6% of individual faeces. Multivariate statistical analyses of over 250 parameters revealed several risk-factors for the presence of STEC and O157:H7. Risk-factors were mainly related to the potential of cross-contamination of feeds and cross-infection of cows, and age of the animals. In general, no significant differences between the two farm types concerning prevalence or risk for carrying STEC or O157:H7 were observed. Because the incidence of human disease caused by STEC in Switzerland is low, the risk that people to get infected appears to be small despite a relatively high prevalence in cattle. Nevertheless, control and prevention practices are indicated to avoid contamination of animal products.

Coccolithophore taxa and PCA factor analysis of ODP Site 1233

We provide high-resolution sea surface temperature (SST) and paleoproductivity data focusing on Termination 1. We describe a new method for estimating SSTs based on multivariate statistical analyses performed on modern coccolithophore census data, and we present the first downcore reconstructions derived from coccolithophore assemblages at Ocean Drilling Project (ODP) Site 1233 located offshore Chile. We compare our coccolithophore SST record to alkenone-based SSTs as well as SST reconstructions based on dinoflagellates and radiolaria. All reconstructions generally show a remarkable concordance. As in the alkenone SST record, the Last Glacial Maximum (LGM, 19-23 kyr B.P.) is not clearly defined in our SST reconstruction. After the onset of deglaciation, three major warming steps are recorded: from 18.6 to 18 kyr B.P. (~2.6°C), from 15.7 to 15.3 kyr B.P. (~2.5°C), and from 13 to 11.4 kyr B.P. (~3.4°C). Consistent with the other records from Site 1233 and Antarctic ice core records, we observed a clear Holocene Climatic Optimum (HCO) from ~8-12 kyr B.P. Combining the SST reconstruction with coccolith absolute abundances and accumulation rates, we show that colder temperatures during the LGM are linked to higher coccolithophore productivity offshore Chile and warmer SSTs during the HCO to lower coccolithophore productivity, with indications of weak coastal upwelling. We interpret our data in terms of latitudinal displacements of the Southern Westerlies and the northern margin of the Antarctic Circumpolar Current system over the deglaciation and the Holocene.