1,721,189 research outputs found
Which are the factors controlling tree seedling establishment in North Italian floodplain forests invaded by non-native tree species?
No full textIn hardwood floodplain forests of the North Italian Po Plain the non-native and light-demanding tree species Prunus serotina Ehrh. and Robinia pseudoacacia L. coexist with the native tree species Carpinus betulus L., Quercus robur L., and Ulmus minor Mill. In order to identify the factors controlling the establishment of seedlings of these species, we focused on the scale of micro-plots, which provide safe sites for tree species regeneration. We used seedling and sapling counts as the response variable. For modelling the seedling regeneration in relation to a multivariate set of 15 measured soil and stand characteristics, a hurdle negative binomial model was applied and then compared with a non-metric multidimensional scaling ordination, visualising the relationships between the regenerating species and the environmental parameters. In general, it could be shown that there are species-specific differences in the requirements for seedling regeneration between the five target species, and that the most important parameters affecting seedling establishment were the availability of potential seed sources, soil humidity, and light availability. Q. robur and C. betulus showed a wide ecological range regarding soil humidity, whereas U. minor was restricted to moister soils, and the two non-native species only occurred on dry soils. In addition, R. pseudoacacia and Q. robur regenerated very scarcely under the closed canopy inside the stands and were highly dependent on large scale disturbance events. After a disturbance, the presence of R. pseudoacacia in the canopy promoted the seedling regeneration of Q. robur. P. serotina was found to regenerate frequently in the closed forest and to persist for a long time under shade, but also needs forest gaps to establish in the canopy. We believe that P. serotina was wrongly classified as a shade-intolerant species in the past. We suggest that it is a competitive invader in a broad range of resource availability. In conclusion, with regard to the further forest development, it could be assumed that the absence of disturbance events resulting in large openings leads to a reduction in the number of P. serotina, R. pseudoacacia, and Q. robur individuals during succession. Nevertheless, disturbances on wetter soils, e.g. related to the natural river dynamics, will clearly favour the seedling regeneration of Q. robur compared to the non-native species, which are generally limited to the drier sites of the floodplain forests
Structured Additive Regression Models: An R Interface to BayesX
Full text linkStructured additive regression (STAR) models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms), fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference
Conditional Variable Importance for Random Forests
Full text linkRandom forests are becoming increasingly popular in many scientific fields because they can cope with ``small n large p'' problems, complex interactions and even highly correlated predictor variables. Their variable importance measures have recently been suggested as screening tools for, e.g., gene expression studies. However, these variable importance measures show a bias towards correlated predictor variables. We identify two mechanisms responsible for this finding: (i) A preference for the selection of correlated predictors in the tree building process and (ii) an additional advantage for correlated predictor variables induced by the unconditional permutation scheme that is employed in the computation of the variable importance measure. Based on these considerations we develop a new, conditional permutation scheme for the computation of the variable importance measure. The resulting conditional variable importance is shown to reflect the true impact of each predictor variable more reliably than the original marginal approach
Automatic generation of simple (statistical) exams
No full textPackage exams provides a framework for automatic generation of simple (statistical) exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R code for data generation and LaTeX code for problem and solution description) and the master file is a LaTeX document with some additional control commands. This paper gives an overview on the main design aims and principles as well as strategies for adaptation and extension. Hands-on illustrations - based on example exercises and control files provided in the package - are presented to get new users started easily. (author´s abstract)Series: Research Report Series / Department of Statistics and Mathematic
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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