Logistic regression for clustered data from environmental monitoring programs

Exploring Help-Seeking Predictors Among Colombian Victims of Intimate Partner Violence in Different Age Groups

This study examined the extent, source, and individual, microsystem, exosystem, and macrosystem-level (Heise's Ecological Model) predictors of help-seeking behaviors among women of different age groups in Colombia. Data on 12,915 married or cohabitating women who had experienced diverse forms of intimate partner violence (IPV) were obtained from the Colombia Demographic Health Survey 2015. More than half of the Colombian women who reported some form of IPV experience did not seek any help. Women aged 25-39 were less likely to seek help. Type of violence and experience of IPV and education were the strongest predictors of help-seeking among women in all age groups. Implications for research and programming are discussed.

Multiple drivers of large-scale lichen decline in boreal forest canopies

Thin, hair-like lichens (Alectoria, Bryoria, Usnea) form conspicuous epiphyte communities across the boreal biome. These poikilohydric organisms provide important ecosystem functions and are useful indicators of global change. We analyse how environmental drivers influence changes in occurrence and length of these lichens on Norway spruce (Picea abies) over 10 years in managed forests in Sweden using data from >6000 trees. Alectoria and Usnea showed strong declines in southern-central regions, whereas Bryoria declined in northern regions. Overall, relative loss rates across the country ranged from 1.7% per year in Alectoria to 0.5% in Bryoria. These losses contrasted with increased length of Bryoria and Usnea in some regions. Occurrence trajectories (extinction, colonization, presence, absence) on remeasured trees correlated best with temperature, rain, nitrogen deposition, and stand age in multinomial logistic regression models. Our analysis strongly suggests that industrial forestry, in combination with nitrogen, is the main driver of lichen declines. Logging of forests with long continuity of tree cover, short rotation cycles, substrate limitation and low light in dense forests are harmful for lichens. Nitrogen deposition has decreased but is apparently still sufficiently high to prevent recovery. Warming correlated with occurrence trajectories of Alectoria and Bryoria, likely by altering hydration regimes and increasing respiration during autumn/winter. The large-scale lichen decline on an important host has cascading effects on biodiversity and function of boreal forest canopies. Forest management must apply a broad spectrum of methods, including uneven-aged continuous cover forestry and retention of large patches, to secure the ecosystem functions of these important canopy components under future climates. Our findings highlight interactions among drivers of lichen decline (forestry, nitrogen, climate), functional traits (dispersal, lichen colour, sensitivity to nitrogen, water storage), and population processes (extinction/colonization).

Machine Learning Algorithms for Temperature Management in the Anaerobic Digestion Process

Process optimization is no longer an option for processes, but an obligation to survive in the market in any industry. This argument also applies to anaerobic digestion in biogas plants. The contribution of biogas plants to renewable energy can be increased through more productive systems with less waste, which brings the common goal of minimizing costs and maximizing yields in processes. With the help of data science and predictive analytics, it is possible to take conventional process optimization and operational excellence methods, such as statistical process control and Six Sigma, to the next level. The more advanced the process optimization aspect, the more transparent and responsive the systems. In this study, seven different machine learning algorithms—linear regression, logistic regression, K-NN, decision trees, random forest, support vector machine (SVM) and XGBoost—were compared with laboratory results to define and predict the possible impacts of wide range temperature fluctuations on process stability. SVM provided the best accuracy with 0.93 according to the metric precision of the models calculated using the confusion matrix.

Convergence Theorems in Multinomial Saturated and Logistic Models

In  this  paper,  we  develop  a  theoretical study about the  logistic  and saturated multinomial models when the response  variable takes  one of R ≥ 2 levels.  Several  theorems on the  existence  and  calculations of the  maximum likelihood  (ML)  estimates of the  parameters of both  models  are  presented and  demonstrated. Furthermore, properties are identified and,  based  on an asymptotic  theory,  convergence theorems are  tested for  score  vectors  and information matrices of both  models.  Finally, an application of this  theory is presented and  assessed  using data from the  R statistical program.

Characterizing the Spatial Distribution of Eragrostis Curvula (Weeping Lovegrass) in New Jersey (United States of America) Using Logistic Regression

The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.