Bayesian multilevel models for repeated measures data : a conceptual and practical introduction in R /: a conceptual and practical introduction in R. (2023)

• Record Type: Book
• Further Information: Note: Santiago Barreda, Noah Silbert.
• Authors: Barreda, Santiago; Silbert, Noah
• Contents: Preface Acknowledgments 1. Introduction: Experiments and Variables 2. Probabilities, Likelihood, and Inference 3. Fitting Bayesian Regression Models with brms 4. Inspecting a ‘Single Group’ of Observations using a Bayesian Multilevel Model 5. Comparing Two Groups of Observations: Factors and Contrasts 6. Variation in Parameters (‘Random Effects’) and Model Comparison 7. Comparing Many Groups, Interactions, and Posterior Predictive Checks 8. Varying Variances, More about Priors, and Prior Predictive Checks 9. Quantitative Predictors and their Interactions with Factors 10. Logistic Regression and Signal Detection Theory Models 11. Multiple Quantitative Predictors, Dealing with Large Models, and Bayesian ANOVA 12. Multinomial and Ordinal Regression 13. Writing up Experiments: An investigation of the Perception of Apparent Speaker Characteristics from Speech Acoustics
• Edition: 1st
• Publisher Details: London : Routledge
• Publication Date: 2023
• Extent: 1 online resource (464 pages), illustrations (colour)
• Subjects: 519.542; Bayesian statistical decision theory; R (Computer program language); Multilevel models (Statistics)
• Languages: English
• ISBNs: 9781000869835; 9781000869781
• Related ISBNs: 9781032259628; 9781032259635
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.