Xavier Bry

Genetic determinism of anatomical and hydraulic traits within an apple progeny

The apple tree is known to have an isohydric behaviour, maintaining rather constant leaf water potential in soil with low water status and/or under high evaporative demand. However, little is known on the xylem water transport from roots to leaves from the two perspectives of efficiency and safety, and on its genetic variability. We analysed 16 traits related to hydraulic efficiency and safety, and anatomical traits in apple stems, and the relationships between them. Most variables were found heritable, and we investigated the determinism underlying their genetic control through a quantitative trait loci (QTL) analysis on 90 genotypes from the same progeny. Principal component analysis (PCA) revealed that all traits related to efficiency, whether hydraulic conductivity, vessel number and area or wood area, were included in the first PC, whereas the second PC included the safety variables, thus confirming the absence of trade-off between these two sets of traits. Our results demonstrated that clustered variables were characterized by common genomic regions. Together with previous results on the same progeny, our study substantiated that hydraulic efficiency traits co-localized with traits identified for tree growth and fruit production.

Harpoon or Bait? A Comparison of Various Metrics in Fishing for Sequence Patterns

The use of sequence analysis in the social sciences has significantly increased during the last decade or two. Sequence analysis explores and describes trajectories and “fishes for patterns” (Abbott, 2000). Many dissimilarity metrics exist in various domains (bioinformatics, data mining, etc.); therefore a crucial and pervasive issue in papers using sequence analysis is robustness. To what extent do the various techniques lead to consistent and converging results? What kinds of patterns are more easily fished out by each of the metrics? Here we propose a systematic comparison of about ten metrics that have been used in the social science literature, based on the examination of dissimilarity matrices computed from a simulated sequence data set including various patterns that sociologists can try to identify. This should help scholars in picking the method best suited to their data design and inquiry objectives.

Supervised component generalized linear regression using a PLS-extension of the Fisher scoring algorithm

In the current estimation of a GLM model, the correlation structure of regressors is not used as the basis on which to lean strong predictive dimensions. Looking for linear combinations of regressors that merely maximize the likelihood of the GLM has two major consequences: (1) collinearity of regressors is a factor of estimation instability, and (2) as predictive dimensions may lean on noise, both predictive and explanatory powers of the model are jeopardized. For a single dependent variable, attempts have been made to adapt PLS regression, which solves this problem in the classical Linear Model, to GLM estimation. In this paper, we first discuss the methods thus developed, and then propose a technique, Supervised Component Generalized Linear Regression (SCGLR), that combines PLS regression with GLM estimation in the multivariate context. SCGLR is tested on both simulated and real data.

THEME-SEER: a multidimensional exploratory technique to analyze a structural model using an extended covariance criterion

In this work, we present a new approach to path modeling based on an extended multiple covariance criterion: system extended multiple covariance (SEMC). SEMC is suitable to measure the quality of any structural equations system. We show why SEMC may be preferred to criteria based on usual covariance of components and also to criteria based on residual sums of squares. We give a pursuit algorithm ensuring that SEMC increases and converges. When one wishes to extract more than one component per variable group, a problem arises of component hierarchy. To solve it, we define a local nesting principle of component models that makes the role of each component statistically clear. We then embed the pursuit algorithm in a more general algorithm that extracts sequences of locally nested models. We finally provide a component backward selection strategy. The technique is applied to cigarette data to model the generation of chemical compounds in smoke through tobacco combustion. Copyright

Analyse biographique des groupes. Les avancées d'une recherche en cours

Lelievre (Eva), Bonvalet (Catherine), Bry (Xavier). - Analisis biografico de grupos. Avances de una investigacion en curso El analisis biografico, que ha conducido a la redefinicion de las bases del analisis de- mografico en terminos de analisis de procesos estocasticos complejos (Courgeau y Lelievre; 1989; 1996), se aplica a datos individuals longitudinales. Lo ideal seria poder situar cada trayectoria en un contexto amplio y realizar un analisis de los procesos demograficos indi- viduales tomando en consideracion los acontecimientos adyacentes о concurrentes que con- ciernen al entorno del individuo. El paso del individuo a su entorno en la modelizacion biografica, tanto en la recopilacion de datos como en el analisis, nos lleva a reflexionar de nuevo sobre los elementos tornados en consideracion en el analisis longitudinal. Es necesa- rio hallar un compromiso entre la operacionalidad de los conceptos y la coherencia de los analisis, tanto desde el punto de vista de la teoria como desde el punto de vista de los mo- delos: el articulo presenta de forma sucesiva los avances formales y los resultados mas apli- cados de esta investigacion en curso.

Exploring a physico-chemical multi-array explanatory model with a new multiple covariance-based technique: structural equation exploratory regression.

In this work, we consider chemical and physical variable groups describing a common set of observations (cigarettes). One of the groups, minor smoke compounds (minSC), is assumed to depend on the others (minSC predictors). PLS regression (PLSR) of m inSC on the set of all predictors appears not to lead to a satisfactory analytic model, because it does not take into account the experts knowledge. PLS path modeling (PLSPM) does not use the multidimensional structure of predictor groups. Indeed, the expert needs to separate the influence of several pre-designed predictor groups on minSC, in order to see what dimensions this influence involves. To meet these needs, we consider a multi-group component-regression model, and propose a method to extract from each group several strong uncorrelated components that fit the model. Estimation is based on a global multiple covariance criterion, used in combination with an appropriate nesting approach. Compared to PLSR and PLSPM, the structural equation exploratory regression (SEER) we propose fully uses predictor group complementarity, both conceptually and statistically, to predict the dependent group.

Explorer l'explicatif: application a l'analyse biographique

This article presents an empirical plugging of factor analysis and generalized linear regression (logistic regression, Cox models, ...). We show that this combination can facilitate the exploration of complex data such as that on event histories (time-varying, censored) for modelling purposes. By combining a regression method with a new type of factor analysis ? Thematic Components Analysis ? we show how an explanatory conceptual model for the data can be included from the start of the exploratory phase. This method is then applied to an analysis of the divorce behaviour of men in Dakar, and used to give a simple illustration of each methodological point discussed.

A “Global Interdependence” Approach to Multidimensional Sequence Analysis

Although sequence analysis has now become a widespread approach in the social sciences, several strategies have been developed to handle the specific issue of multidimensional sequences. These strategies have distinct characteristics related to the way they explicitly emphasize multidimensionality, interdependence, and parsimony. In this context, the authors introduce an original approach based on structural links between the dimensions, combining optimal matching analysis, multidimensional scaling, canonical partial least squares, and clustering, an approach the authors call globally interdependent multiple sequence analysis (GIMSA). The authors then apply GIMSA to mother-daughter employment histories in France and discuss the value of this method.

THEME: THEmatic model exploration through multiple co-structure maximization

After showing that plain covariance or correlation‐based criteria are generally not suitable to deal with multiple‐block component models in an exploratory framework, we propose an extended criterion: multiple co‐structure (MCS). MCS combines the goodness‐of‐fit indicator of the component model to a flexible measure of structural relevance of the components. Thus, it allows to track various kinds of interpretable structures within the data, on top of variance–maximizing components: variable‐bundles, components close to satisfying relevant structural constraints, and so on. MCS is to be maximised under unit‐norm constraints on coefficient‐vectors. We provide a dedicated ascent algorithm for it. This algorithm is nested into a more general one, named THEME (thematic equation model explorator), which calculates several components per data‐array and extracts nested structural component models. The method is tested on simulated data and applied to physicochemical data. Copyright

Multidimensional Exploratory Analysis of a Structural Model Using a Class of Generalized Covariance Criteria

Our aim is to explore a structural model: several variable groups describing the same observations are assumed to be structured around latent dimensions that are linked through a linear model that may have several equations. This type of model is commonly dealt with by methods assuming that the latent dimension in each group is unique. However, conceptual models generally link concepts which are multidimensional. We propose a general class of criteria suitable to measure the quality of a Structural Equation Model (SEM). This class contains the covariance criteria used in PLS Regression and the Multiple Covariance criterion of the SEER method. It also contains quartimax-related criteria. All criteria in the class must be maximized under a unit norm constraint. We give an equivalent unconstrained maximization program, and algorithms to solve it. This maximization is used within a general algorithm named THEME (Thematic Equation Model Exploration), which allows to search the structures of groups for all dimensions useful to the model. THEME extracts locally nested structural component models.