tinyVAST: R package with an expressive interface to specify lagged and simultaneous effects in multivariate spatio-temporal models
arxiv(2024)
摘要
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.
From the user viewpoint, tinyVAST adapts a widely used formula interface to
specify generalized 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 generalized linear mixed model
(GLMM). This expressive interface encompasses vector autoregressive, empirical
orthogonal functions, spatial factor analysis, and ARIMA models. 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 assumptions
for research and real-world policy evaluation.
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