GLMMs, GAMMs
Table of Contents
Description & principle
A clear, technical yet accessible explanation of the method, its core principle(s).
Major variants
optional
If the method has variants that seem important, either already widespread or promising and well documented.
Further online resources
References to useful online resources to get started, e.g. explanation blogs
Reference articles
Method
- One or a few key academic references that introduce or formalize the method.
Research applications
With RS data in Ecology / Biodiversity
- A
Without RS data (Ecology domain)
optional
- B
Packages
Python
R
Code Cells
optional
Assessment table
| Category | Criteria | Assessment |
|---|---|---|
| Outcome | Objective | Effect estimation, Predictive task + interpretability |
| Estimand | CATE, Others, Maps & generalisations | |
| Validity | Varying | |
| Data compatibility | Type | Spatial only (cross-sectional), Time-series (one sample), Inapplicable |
| Required TS length | ≥ 10 | |
| Handles few samples | 10 to 100 | |
| Handles huge datasets (n) | Most do | |
| Handles missing data | No: requires prelim. correction | |
| RS-data proven | Few applications | |
| Assumptions | Functional form | Linear, Additivity, Assumption-free |
| No unobserved confounders | Recommended | |
| No interference | Required | |
| Well-defined treatments | Required | |
| Common support (positivity) | Required | |
| Causal Markov Condition | Recommended | |
| Faithfulness | Required | |
| IDD | Relaxes assumption | |
| Model specific assumption | Normality of random effects | |
| Model properties | Requires explicit processes | Agnostic |
| Exposure type | Binary, Categorical, Continuous / Time-varying, Multivariate | |
| Number of variables | Multivariate | |
| Handles lag effects | No | |
| Propagates uncertainty | Model-specific tools | |
| Parametric nature | Parametric, Semi-parametric | |
| Packages | Language | R, Python |
| Usage | Technical but well documented |