<scp> <i>tinyVAST</i> </scp> : R Package With an Expressive Interface to Specify Lagged and Simultaneous Effects in Multivariate Spatio‐Temporal Models
Bibliographic record
Abstract
ABSTRACT Aim Multivariate spatio‐temporal models are widely applicable, but specifying their structure is complicated and may inhibit wider use. We introduce the R package tinyVAST from two viewpoints: the software user and the statistician. Innovation From the user viewpoint, tinyVAST adapts a widely used formula interface to specify generalised additive models and combines this with arguments to specify spatial and spatio‐temporal interactions among variables. These interactions are specified using arrow notation (from structural equation models) or an extended arrow‐and‐lag notation that allows simultaneous, lagged and recursive dependencies among variables over time. The user also specifies a spatial domain for areal (gridded), continuous (point‐count) or stream‐network data. From the statistician viewpoint, tinyVAST constructs sparse precision matrices representing multivariate spatio‐temporal variation, and parameters are estimated by specifying a generalised linear mixed model (GLMM). This expressive interface encompasses vector autoregressive, empirical orthogonal functions, spatial factor analysis and ARIMA models. Main Conclusion To demonstrate, we fit to data from two survey platforms sampling corals, sponges, rockfishes and flatfishes in the Gulf of Alaska and Aleutian Islands. We then compare eight alternative model structures using different assumptions about habitat drivers and survey detectability. Model selection suggests that towed‐camera and bottom trawl gears have spatial variation in detectability but sample the same underlying density of flatfishes and rockfishes and that rockfishes are positively associated with sponges while flatfishes are negatively associated with corals. We conclude that tinyVAST can be used to test complicated dependencies representing alternative structural hypotheses for research and real‐world policy evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".