Applied statistical modelling for ecologists : a practical guide to Bayesian and likelihood inference using R, JAGS, NIMBLE, Stan and TMB /
"Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software...
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| Hlavní autoři: | , |
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| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam, Netherlands ; Cambridge, MA, United States :
Elsevier,
[2024]
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| Témata: | |
| On-line přístup: | ScienceDirect |
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Obsah:
- Introduction
- Introduction to statistical inference
- Linear regression models and their extensions to generalized linear, hierarchical, and integrated models
- Introduction to general-purpose model-fitting engines and the "model of the mean"
- Normal linear regression
- Comparing two groups in a normal model
- Models with a single categorical covariate with more than two levels
- Comparisons along two classifications in a model with two factors
- General linear model for a normal response with continuous and categorical explanatory variables
- Linear mixed-effects model
- Introduction to the generalized linear model (GLM) : comparing two groups in a Poisson regression
- Overdispersion, zero-inflation and offsets in a Poisson GLM
- Poisson GLM with continuous and categorical explanatory variables
- Poisson generalized linear mixed model, or Poisson GLMM
- Comparing two groups in a logistic regression model
- Binomial GLM with continuous and categorical explanatory variables
- Binomial generalized linear mixed model
- Model building, model checking, and model selection
- Occupancy models
- Integrated models
- Conclusion.