Bayesian Additive Regression Trees with Model Trees

Bayesian Additive Regression Trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of non-linearity and high-order interactions. In this paper, we introduce an extension of BART, called Model Trees BART (MOTR-BART), that considers piecewise linear functions at node levels instead of piecewise constants. In MOTR-BART, rather than having a unique value at node level for the prediction, a linear predictor is estimated considering the covariates that have been used as the split variables in the corresponding tree. In our approach, local linearities are captured more efficiently and fewer trees are required to achieve equal or better performance than BART. Via simulation studies and real data applications, we compare MOTR-BART to its main competitors. R code for MOTR-BART implementation is available at https://github.com/ebprado/MOTR-BART

lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time

Background and Objective: Observational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples. Methods: The longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy functions can be estimated based on fixed effects and variance components of the mixed-effects regression model. Inference is made through bootstrap confidence intervals and diagnostic can be done via plots, and statistical tests. Results: The main features of the package are estimation and inference about the extent of agreement using numerical and graphical summaries. Moreover, our approach accommodates both balanced and unbalanced experimental designs or observational studies, and allows for different within-group error structures, while allowing for the inclusion of covariates in the linear predictor to control systematic variations in the response. All examples show that our methodology is flexible and can be applied to many different data types. Conclusions: The lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases

Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package, and a comparison with traditional approaches

BEF studies aim at understanding how ecosystems respond to a gradient of species diversity. Diversity-Interactions models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The no. of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. We compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. DI models can capture variation due to species identities, species proportions and species interactions, in addition to richness effects. We also introduce the DImodels R package for implementing DI models. Through worked examples, we show that using DI models can lead to considerably improved model fit over other methods. Collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions. Overall, DI models lead to enhanced inference compared to other approaches. Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends, however, there are multiple ways to quantify the species diversity of a community. DI modelling provides a framework to test the multiple aspects of species diversity and facilitates uncovering a deeper ecological understanding of the BEF relationship

2007 Symposium Announcement

The fungal genera Metarhizium and Beauveria are considered as both entomopathogens and endophytes; they are able to colonize a wide variety of plants and can cause increased plant growth and protect plants against pests. In view of the need for new biological methods for plant protection and how promising and little studied candidates entomopathogens are, the aim of this research was to evaluate the potential of two isolates of Metarhizium robertsii (ESALQ 1622) and Beauveria bassiana (ESALQ 3375) to suppress spider mite Tetranychus urticae population growth and ability to promote growth of bean plants Phaseolus vulgaris after seed treatment, in order to develop an innovative strategy by using these fungi as inoculants to improve both spider mites control and plant growth and yield. In addition, behavioral responses and predation rates of the predatory mite Phytoseiulus persimilis towards fungal treated plants and spider mites from these plants were also evaluated in leaf disc assays to assess potential conflicting effects of the fungal inoculations on overall pest control at higher trophic levels. Seed inoculations by the two isolates of M. robertsii and B. bassiana were done individually and in combinations to evaluate potential benefits of co-inoculants. The results showed a significant reduction in T. urticae populations and improved plant development when inoculated with M. robertsii and B. bassiana individually and in combination. The predatory mite P. persimilis showed no difference in the predation rate on T. urticae from treated and untreated plants even though the predators were most likely to feed on spider mites from fungal treated plants during the first half of the trial, and on spider mites from control plants during the remainder of the trial. Overall, the two fungal isolates have potential as seed inoculants to suppress spider mites in bean and the strategy appears to have no conflict with use of predatory mites. Co-inoculation of both fungal isolates showed no additional benefits compared to single isolate applications under the given test conditions

A Mixed Model for Assessing the Effect of Numerous Plant Species Interactions on Grassland Biodiversity and Ecosystem Function Relationships

In grassland ecosystems, it is well known that increasing plant species diversity can improve ecosystem functions (i.e., ecosystem responses), for example, by increasing productivity and reducing weed invasion. Diversity-Interactions models use species proportions and their interactions as predictors in a regression framework to assess biodiversity and ecosystem function relationships. However, it can be difficult to model numerous interactions if there are many species, and interactions may be temporally variable or dependent on spatial planting patterns. We developed a new Diversity-Interactions mixed model for jointly assessing many species interactions and within-plot species planting pattern over multiple years. We model pairwise interactions using a small number of fixed parameters that incorporate spatial effects and supplement this by including all pairwise interaction variables as random effects, each constrained to have the same variance within each year. The random effects are indexed by pairs of species within plots rather than a plot-level factor as is typical in mixed models, and capture remaining variation due to pairwise species interactions parsimoniously. We apply our novel methodology to three years of weed invasion data from a 16-species grassland experiment that manipulated plant species diversity and spatial planting pattern and test its statistical properties in a simulation study. Supplementary materials accompanying this paper appear online

Phosphoethanolamine and omega-3 in patients with asthma

The effect of omega-3 (n-3) in asthma has been inconclusive. One explanation for it may be the low incorporation of these fatty acids in clinical studies. Phosphoethanolamine (PEtn) can increase the synthesis of phosphatidylethanolamine, which can, in turn, increase the incorporation of n-3 in cell membranes. The aim of this study is to evaluate the effect of synthetic PEtn in patients with asthma who are receiving n-3. This randomized, double-blind, placebo-controlled study was carried out over a two month period by using spirometry, the Asthma Control Test questionnaire (ACT) and medicine intake. Forty-one patients with asthma were studied. Twenty-one patients received n-3 daily (1.080 mg of EPA, 720 mg of DHA) and 800 mg of PEtn (PEtn group), and twenty patients received the same doses of n-3 and placebo (control group). All patients continued receiving their conventional treatment for asthma. The hospital ethics committee approved the study. Five patients of each group required systemic corticosteroids, being the total consumption, smaller in the PEtn group (127.4 mg of prednisone/patient versus 416.0 mg of prednisone/patient in the control group, p-value = 0.0269). There were no significant differences in the changing of ATC and FEV1, as well as in the intake of formoterol or budesonide between the groups. In this study, patients who received phosphoethanolamine and omega-3 needed a smaller dose of systemic corticosteroid for asthma control than patients who only received omega-3. However, as the trial was conducted on a small scale, more studies are necessary

The ERICE-score: the new native cardiovascular score for the low-risk and aged mediterranean population of Spain

[Abstract] Introduction and objectives. In Spain, data based on large population-based cohorts adequate to provide an accurate prediction of cardiovascular risk have been scarce. Thus, calibration of the EuroSCORE and Framingham scores has been proposed and done for our population. The aim was to develop a native risk prediction score to accurately estimate the individual cardiovascular risk in the Spanish population. Methods. Seven Spanish population-based cohorts including middle-aged and elderly participants were assembled. There were 11 800 people (6387 women) representing 107 915 person-years of follow-up. A total of 1214 cardiovascular events were identified, of which 633 were fatal. Cox regression analyses were conducted to examine the contributions of the different variables to the 10-year total cardiovascular risk. Results. Age was the strongest cardiovascular risk factor. High systolic blood pressure, diabetes mellitus and smoking were strong predictive factors. The contribution of serum total cholesterol was small. Antihypertensive treatment also had a significant impact on cardiovascular risk, greater in men than in women. The model showed a good discriminative power (C-statistic = 0.789 in men and C = 0.816 in women). Ten-year risk estimations are displayed graphically in risk charts separately for men and women. Conclusions. The ERICE is a new native cardiovascular risk score for the Spanish population derived from the background and contemporaneous risk of several Spanish cohorts. The ERICE score offers the direct and reliable estimation of total cardiovascular risk, taking in consideration the effect of diabetes mellitus and cardiovascular risk factor management. The ERICE score is a practical and useful tool for clinicians to estimate the total individual cardiovascular risk in Spain.[Resumen] Introducción y objetivos. En España no existen unas cohortes poblacionales suficientemente grandes para hacer predicciones precisas del riesgo cardiovascular. Las ecuaciones de Framingham y EuroSCORE calibradas son las más utilizadas en España. El objetivo es desarrollar la primera ecuación de predicción autóctona para estimar con precisión el riesgo cardiovascular individual en España. Métodos. Análisis conjunto de siete cohortes españolas de población de mediana edad y anciana. La población del estudio —11.800 personas (6.387 mujeres)— aportó un total de 107.915 personas-año de seguimiento y 1.214 eventos cardiovasculares (633 de ellos, mortales). Se efectuó un análisis de regresión de Cox para examinar la contribución de los diferentes factores al riesgo de cualquier evento cardiovascular (mortal y no mortal). Resultados. La edad fue el principal factor de riesgo de eventos cardiovasculares. La presión arterial sistólica, la diabetes mellitus, el tabaquismo y el tratamiento antihipertensivo fueron factores predictivos fuertemente asociados con el riesgo cardiovascular. En cambio, la contribución del colesterol total sérico fue pequeña, especialmente en los mayores de 70 años. El modelo final de riesgo mostró un buen poder discriminatorio (estadístico C = 0,789 en varones y C = 0,816 en mujeres). Conclusiones. ERICE es una nueva ecuación de riesgo cardiovascular genuinamente española obtenida a partir del riesgo concurrente individual de los participantes en varias cohortes. La ecuación ERICE ofrece una estimación directa y fiable del riesgo cardiovascular total teniendo en cuenta factores como la diabetes mellitus y el tratamiento farmacológico de los factores de riesgo cardiovascular, habitualmente no incluidos en otras ecuaciones.Instituto de Salud Carlos III; G03/065Instituto de Salud Carlos III; PI05/1464Instituto de Salud Carlos III; RD06/0014/001