An examination of predictor variables for child compliance and noncompliance

This research aims at analyzing (1) the effect of vendor’s ability, benevolence, and integrity variables toward e-commerce customers’ trust in UBM; (2) the effect of vendor’s ability, benevolence, and integrity variables toward the level of e-commerce customers’ participation in Indonesia; and (3) the effect of trust variable toward level of e-commerce customers participation in UBM. This research makes use of UBM e-commerce users as research samples while using Likert scale questionnaire for data collection. Furthermore, the questionnaires are sent to as many as 200 respondents. For data analysis method, Structural Equation Model was used. Out of three predictor variables (ability, benevolence, and integrity), it is only vendor’s integrity that has a positive and significant effect on customers’ trust. On the other hand, it is only vendor’s integrity and customer’s trust that have a positive and significant effect on e-commerce customers’ participation in UBM. Keywords: e-commerce customers’ participation, ability, benevolence, integrity

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Natural Protected Areas as Providers of Ecological Connectivity in the Landscape: The Case of the Iberian Lynx

Sustainability ◽

10.3390/su13010041 ◽

2020 ◽

Vol 13 (1) ◽

pp. 41

Author(s):

Iván Barbero-Bermejo ◽

Gabriela Crespo-Luengo ◽

Ricardo Enrique Hernández-Lambraño ◽

David Rodríguez de la Cruz ◽

José Ángel Sánchez-Agudo

Keyword(s):

Predictor Variables ◽

Land Uses ◽

Vegetation Types ◽

Iberian Lynx ◽

Natural Protected Areas ◽

Ecological Connectivity ◽

Historical Distribution ◽

Gis Environment ◽

Conservation Plans

The design of conservation plans for the improvement of habitats of threatened species constitutes one of the most plausible possibilities of intervention in the structure and composition of the landscape of a large territory. In this work we focus on the Iberian lynx in order to establish potential ecological corridors using ecoinformatic tools from the GIS environment to improve connectivity between the existing natural spaces within the scope of its historical distribution. We processed 669 records of the presence of the lynx and six predictor variables linked to the habitat of the species. With this, corridors have been generated between natural areas. The determination of possible bottlenecks or dangerous areas (e.g., hitches on highways) allows for focusing efforts on their conservation. This type of approach seeks to improve efficiency in the design of measures aimed at expanding the territory’s capacity to host its populations, improving both its viability and that of all the other species that are linked to it. The proposals for action on the specific areas defined by the models elaborated in this work would imply interventions on the land uses and existing vegetation types in order to improve connectivity throughout the territory and increase the resilience of its ecosystems.

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Using Ultrasound and Inflammation to Improve Prediction of Ischemic Stroke: A Secondary Analysis of the Multi-Ethnic Study of Atherosclerosis

Cerebrovascular Diseases Extra ◽

10.1159/000514373 ◽

2021 ◽

pp. 37-43

Author(s):

Hediyeh Baradaran ◽

Alen Delic ◽

Ka-Ho Wong ◽

Nazanin Sheibani ◽

Matthew Alexander ◽

...

Keyword(s):

Ischemic Stroke ◽

Goodness Of Fit ◽

Primary Outcome ◽

Stroke Risk ◽

Secondary Analysis ◽

Predictor Variables ◽

Baseline Model ◽

Fit Statistics ◽

Ischemic Stroke Risk

Introduction: Current ischemic stroke risk prediction is primarily based on clinical factors, rather than imaging or laboratory markers. We examined the relationship between baseline ultrasound and inflammation measurements and subsequent primary ischemic stroke risk. Methods: In this secondary analysis of the Multi-Ethnic Study of Atherosclerosis (MESA), the primary outcome is the incident ischemic stroke during follow-up. The predictor variables are 9 carotid ultrasound-derived measurements and 6 serum inflammation measurements from the baseline study visit. We fit Cox regression models to the outcome of ischemic stroke. The baseline model included patient age, hypertension, diabetes, total cholesterol, smoking, and systolic blood pressure. Goodness-of-fit statistics were assessed to compare the baseline model to a model with ultrasound and inflammation predictor variables that remained significant when added to the baseline model. Results: We included 5,918 participants. The primary outcome of ischemic stroke was seen in 105 patients with a mean follow-up time of 7.7 years. In the Cox models, we found that carotid distensibility (CD), carotid stenosis (CS), and serum interleukin-6 (IL-6) were associated with incident stroke. Adding tertiles of CD, IL-6, and categories of CS to a baseline model that included traditional clinical vascular risk factors resulted in a better model fit than traditional risk factors alone as indicated by goodness-of-fit statistics. Conclusions: In a multiethnic cohort of patients without cerebrovascular disease at baseline, we found that CD, CS, and IL-6 helped predict the occurrence of primary ischemic stroke. Future research could evaluate if these basic ultrasound and serum measurements have implications for primary prevention efforts or clinical trial inclusion criteria.

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The relationship between scale and predictor variables in species distribution models applied to conservation

Biodiversity and Conservation ◽

10.1007/s10531-021-02176-w ◽

2021 ◽

Author(s):

M. R. Oliveira ◽

W. M. Tomas ◽

N. M. R. Guedes ◽

A.T. Peterson ◽

J. K. Szabo ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Predictor Variables ◽

Distribution Models ◽

The Relationship

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Surrogate minimal depth as an importance measure for variables in random forests

Bioinformatics ◽

10.1093/bioinformatics/btz149 ◽

2019 ◽

Vol 35 (19) ◽

pp. 3663-3671 ◽

Cited By ~ 6

Author(s):

Stephan Seifert ◽

Sven Gundlach ◽

Silke Szymczak

Keyword(s):

Variable Selection ◽

Supplementary Information ◽

Predictor Variables ◽

Importance Measure ◽

Surrogate Variables ◽

Machine Learning Approach ◽

Complex Relationships ◽

Minimal Depth ◽

Insight Into ◽

Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

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