Introduction to random fields

class: center, middle, inverse, title-slide

.title[
# Introduction to random fields
]
.subtitle[
## IMR sdmTMB workshop
]
.author[
### 
]
.date[
### May 23–25 2023
]

---

-->
 -->
 -->
 -->

# Plan for the 3-day course

* Day 1: Intro to random fields, intro to sdmTMB, spatial models

* Day 2: Spatiotemporal models

* Day 3: Time-varying effects, spatially varying effects, index standardization

---

# Plan for the 3-day course

* Each day: about 75 minutes of lectures, 5 minute break, ~60 minutes of exercises, ~25 minute regroup

* Have questions? Use the [Google Doc](https://docs.google.com/document/d/12ZVzOmjbBa8VVTK0tmaNEcN1eP93g6iagDM2c8sSsfo/edit?usp=sharing), ask during any pause, or at the end of each day. Thanks!

---

# Motivating questions

* Data often have spatial attributes

* Ideal world:
  * Plug spatial covariates into a GLM / GLMM
  * Residuals are uncorrelated  
  
<img src="01-intro-random-fields_files/figure-html/sim-rf-intro-1.png" width="700px" style="display: block; margin: auto;" />

---

# Reality
  
* Residual spatial autocorrelation

---

# Modeling spatial autocorrelation

* Need 'wiggly'/smooth surface for approximating all spatial variables missing from model ('latent' variables)

* Several approaches exist
  * 2D smooths in mgcv
  * Random fields and the Stochastic Partial Differential Equation (SPDE)

---

# Spatial smoothing with GAMs (mgcv)

```r
gam(y ~ s(lon, lat) + s(time), ...)

gam(y ~ s(lon, lat) + s(time) + 
      ti(lon, lat, time, d=c(2,1)), ...)

gam(y ~ s(lon, lat, by = "time") + 
      s(time), ...)
```

---

# Spatial smoothing with SPDE (INLA, inlabru)

* SPDE differs in that it explicitly estimates meaningful parameters for spatial covariance function

* SPDE and GAMs can produce the identical results

.small[
Miller, D.L., Glennie, R. & Seaton, A.E. Understanding the Stochastic Partial Differential Equation Approach to Smoothing. JABES 25, 1–16 (2020)
]

---

# Matérn covariance

Flexible, can be exponential or Gaussian shape

---

# Predictive process models

* Estimate spatial field as random effects

* High dimensional datasets computationally challenging

* Gaussian process predictive process models:
  * Estimate values at a subset of locations in the time series
  *   'knots', 'vertices', or 'control points'
  * Use covariance function to interpolate from knots to locations of observations

---

# Predictive process models

* More knots (vertical dashed lines) = more wiggliness & parameters to estimate

---

# Spatial data types

* Lattice: gridded data, e.g. interpolated SST from satellite observations

* Areal: data collected in neighboring spatial areas, e.g. commercial catch records by state / county

* Georeferenced: data where observations are associated with latitude and longitude 
  * Locations may be unique or repeated (stations)
---

# Why is space important?

* Data covary spatially (data that are closer are more similar)

* Relationship between distance and covariance can be described with a spatial covariance function

* Covariance function in 2D may be
  * isotropic (same covariance in each direction)
  * anisotropic (different in each direction)

---

# What is a random field?

---
background-image: url("images/eagle.png")
background-position: bottom right
background-size: 35%

# Random field

---
background-image: url("images/beaker.png")
background-position: bottom right
background-size: 35%

# Random field

* A 2 dimensional "Gaussian Process"

* A realization from a multivariate normal distribution with some covariance function

---
background-image: url("images/elmo.png")
background-position: bottom right
background-size: 30%

# Random field

* A way of estimating a wiggly surface to account for spatial and/or spatiotemporal correlation in data.

* Alternatively, a way of estimating a wiggly surface to account for "latent" or unobserved variables.

* As a bonus, it provides useful covariance parameter estimates: spatial variance and the distance at data points are effectively uncorrelated ("range")

---

# Many ways to simulate random fields

* `RandomFields::RFsimulate()` simulates univariate / multivariate fields
* `fields::sim.rf()` simulates random fields on a grid
* `geoR::grf()` simulates random fields with irregular observations
* `glmmfields::sim_glmmfields()` simulates random fields with/without extreme values
* `sdmTMB::sdmTMB_simulate()` simulates univariate fields with `sdmTMB`

???
Homework: try to work through some of these yourself. Make some plots, and see how changing the covariance affects the smoothness of these fields.

---

# Effects of changing variance and range

---

# Effects of adding noise

* Large observation error looks like noise

* `\(\sigma_{obs}\)` >> `\(\sigma_{O}\)`, `\(\sigma_{E}\)`

<img src="01-intro-random-fields_files/figure-html/sim-rf-large_phi-1.png" width="700px" style="display: block; margin: auto;" />
 
---
  
# Moderate observation errors

* `\(\sigma_{obs}\)` = `\(\sigma_{O}\)` = `\(\sigma_{E}\)`
    
<img src="01-intro-random-fields_files/figure-html/sim-rf-med_phi-1.png" width="700px" style="display: block; margin: auto;" />
  
  
---
     
# Small observation errors  
    
* `\(\sigma_{obs}\)` << `\(\sigma_{O}\)`, `\(\sigma_{E}\)`
    
<img src="01-intro-random-fields_files/figure-html/sim-rf-small_phi-1.png" width="700px" style="display: block; margin: auto;" />

---

# Estimating random fields

.small[
* Georeferenced data often involve 1000s or more points

* Like in the 1-D setting, we need to approximate the spatial field 
  * Options include nearest neighbor methods, covariance tapering, etc.

* sdmTMB uses an approach from INLA
  * for VAST users, this is the same
  * INLA books:  
    <https://www.r-inla.org/learnmore/books>
]

---

# INLA and the SPDE approach

.xsmall[
* SPDE: stochastic partial differential equation

* The solution to a specific SPDE is a Gaussian random field (GRF) with Matérn covariance

* This, and sparse precision matrices, let us efficiently fit approximations to GRFs to large spatial datasets

* INLA is software that performs data wrangling for SPDE estimation
  * INLA also performs approximate Bayesian estimation
  * sdmTMB uses INLA to wrangle matrices, but uses TMB for maximum likelihood estimation
]

.tiny[
Lindgren, F., Rue, H., and Lindström, J. 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society: Series B. 73(4): 423–498.
]

---

# Introducing meshes

Implementing the SPDE with INLA requires constructing a 'mesh'

---

# Mesh construction

.small[
* A unique mesh is generally made for each dataset
* Rules of thumb:
  * More triangles = more computation time
  * More triangles = more fine-scale spatial predictions
  * Borders with coarser resolution reduce number of triangles
  * Use minimum edge size to avoid meshes becoming too fine
  * Want fewer vertices than data points
  * Triangle edge size needs to be smaller than spatial range

* "How to make a bad mesh?" [Haakon Bakka's book](https://haakonbakkagit.github.io/btopic114.html)
]

---

# Building your own mesh

* `INLA::inla.mesh.2d()`: lets many arguments be customized

* `INLA::meshbuilder()`: Shiny app for constructing a mesh, provides R code

* Meshes can include barriers / islands / coastlines with shapefiles

* INLA books
<https://www.r-inla.org/learnmore/books>

---

# Simplifying mesh construction in sdmTMB

sdmTMB has a function `make_mesh()` to quickly construct a basic mesh

Details in next set of slides

---

# Example: cutoff = 50km

---

# Example: cutoff = 25km

---

# Example: cutoff = 10km