Uso de R y GRASS para la obtención de mapas de disponibilidad de hábitat mediante análisis factorial de nicho

It is possible to obtain habitat suitability maps using several applications like "Biomapper" v. 3.1.5 (http://www2.unil.ch/biomapper) or the "adehabitat" library v. 1.2.1, developed to be used within R program (http//www.R-project.org)

Validation of order rank scales based on compositional data analysis: a proposal

Usually, psychometricians apply classical factorial analysis to evaluate construct validity of order rank scales. Nevertheless, these scales have particular characteristics that must be taken into account: total scores and rank are highly relevant

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

El bienestar psicológico de los preadolescentes

Research on psychological well-being - as a component of quality of life - has paroduced notable developments during the last two decades. However, few instruments exist that are applicable to the study of psychological well-being among adolescents. This article presents the results of administering un instrument of this kind to a sample of 1590 Catalan adolescents between 12 and 14 years old. The instrument, Huebner's siss scale (Huebner, 1991a, 1994) had already been adapted to study a population of Spanish children (Alsinet, 1998). The results are compared to those of positive and negative affect scales administered to the same sample, and to those obtained by Huebner with a US sample. The results demonstrate good reliability, a consistent factor structure, a goodfunctioning of the scale in the Spanish population, and a pattern of answers consistent with the original results by Huebner