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Supplementary statistical table reporting results of a binomial generalized linear mixed model and model comparison from a behavioral study on mandrills’ learning of two-day time intervals in a naturalistic foraging context.
Title: Supplementary Material for: Mandrills learn two-day time intervals in a naturalistic foraging situation
Authors: Kavel C.D. Ozturk; Martijn Egas; Karline R.L. Janmaat
Author affiliation: Department of Evolutionary and Population Biology, Institute of Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
Note: Author affiliation Department of Cognitive Psychology, Leiden University, Leiden, The Netherlands
Corresponding author: Kavel C.D. Ozturk
Corresponding author email: [email protected]
Institution: University of Amsterdam; Leiden University; ARTIS Amsterdam Royal Zoo
Document type: Supplementary material
Content type: Statistical results table
Table title: Table S1. Summary of results of binomial GLMM with stability issues (Model 1)
Statistical method: Binomial Generalized Linear Mixed Model (GLMM)
Sample size: N = 237
Comparison test: Full model vs. control model comparison
Chi-square: χ2 = 14.266
Degrees of freedom: df = 7
P-value: p = 0.0466
Confidence interval: Bootstrapped 95% confidence interval
Research topic: Learning of two-day time intervals in mandrills in a naturalistic foraging situation
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Habitats Regulations Assessment (HRA) The Mens SAC, Ebernoe Common SAC and Singleton & Cocking Tunnels SAC Direct impacts, impacts to roosts, foraging habitat and flightlines
Year: 2024
Region / City: South Downs National Park, Sussex, UK
Theme: Environmental Impact Assessment, Conservation
Document Type: Screening and Appropriate Assessment Statement
Agency / Institution: South Downs National Park Authority
Author: South Downs National Park Authority
Target Audience: Planning Authorities, Environmental Consultants, Developers
Period of Validity: Not specified
Approval Date: Not specified
Date of Amendments: Not specified
Supplemental Methods and Results for Personalized Prognostic Prediction of Treatment Outcome for Depressed Patients in a Naturalistic Psychiatric Hospital Setting
Document type: Supplementary online content
Related study: Personalized Prognostic Prediction of Treatment Outcome for Depressed Patients in a Naturalistic Psychiatric Hospital Setting: A Comparison of Machine Learning Approaches
Setting: Naturalistic psychiatric hospital setting
Treatment providers: Psychologists, postdoctoral fellows, clinical psychology interns, psychology practicum students, mental health counselors
Treatment format: Group treatment
Adherence assessment: Bi-annual fidelity assessments via live observation and checklists
Adherence rates: 89%–95%
Population: Depressed patients, including individuals with MDD and comorbid diagnoses
Predictor variables: Clinical measures, demographic characteristics, MDD history, comorbid diagnoses, treatment history, psychiatric medication use, physical health variables
Outcome measures: PHQ-9, GAD-7
Statistical methods: Random Forest imputation (missForest in R), Elastic Net Regression, Lasso, Ridge, Random Forest, Support Vector Machines, Bayesian additive trees, Super Learner, adaptive splines
Validation approach: 10-fold cross-validation and hold-out sample testing
Performance metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, squared correlation (r2) with 95% confidence intervals
ESTABLISHING RELIGIOUS ANTHROPOLOGY AGAINST NATURALISTIC ANTHROPOLOGY
Year: 2021
Region / City: Global
Topic: Anthropology, Religion, Philosophy
Document Type: Academic Article
Institution: N/A
Author: N/A
Target Audience: Scholars, Philosophers, Theologians
Period of Validity: Ongoing
Approval Date: N/A
Date of Changes: N/A
Topics: Mandrills foraging Naturalistic