# Spatial modeling

#### James T. Thorson

    library(tinyVAST)
    library(mgcv)
    library(fmesher)
    library(pdp)  # approx = TRUE gives effects for average of other covariates
    library(lattice)
    library(ggplot2)
    set.seed(101)
    options("tinyVAST.verbose" = FALSE)

`tinyVAST` is an R package for fitting vector autoregressive spatio-temporal (VAST) models using a minimal and user-friendly interface. We here show how it can fit spatial autoregressive model. We first simulate a spatial random field and a confounder variable, and simulate data from this simulated process.

    # Simulate a 2D AR1 spatial process with a cyclic confounder w
    n_x = n_y = 25
    n_w = 10
    R_xx = exp(-0.4 * abs(outer(1:n_x, 1:n_x, FUN="-")) )
    R_yy = exp(-0.4 * abs(outer(1:n_y, 1:n_y, FUN="-")) )
    z = mvtnorm::rmvnorm(1, sigma=kronecker(R_xx,R_yy) )

    # Simulate nuissance parameter z from oscillatory (day-night) process
    w = sample(1:n_w, replace=TRUE, size=length(z))
    Data = data.frame( expand.grid(x=1:n_x, y=1:n_y), w=w, z=as.vector(z) + cos(w/n_w*2*pi))
    Data$n = Data$z + rnorm(nrow(Data), sd=1)

    # Add columns for multivariate and temporal dimensions
    Data$var = "density"
    Data$time = 2020

We next construct a triangulated mesh that represents our continuous spatial domain

    # make mesh
    mesh = fm_mesh_2d( Data[,c('x','y')], cutoff = 2 )

    # Plot it
    plot(mesh)

Finally, we can fit these data using `tinyVAST`

    # Define sem, with just one variance for the single variable
    sem = "
      density <-> density, spatial_sd
    "

    # fit model
    out = tinyVAST( data = Data,
               formula = n ~ s(w),
               spatial_domain = mesh,
               control = tinyVASTcontrol(getsd=FALSE),
               space_term = sem)

We can then calculate the area-weighted total abundance:

    # Predicted sample-weighted total
    integrate_output(out, newdata = out$data)
    #>            Estimate          Std. Error Est. (bias.correct) Std. (bias.correct)
    #>          -104.15036            29.90545          -104.15036                  NA
    # integrate_output(out, apply.epsilon=TRUE )
    # predict(out)

    # True (latent) sample-weighted total
    sum( Data$z )
    #> [1] -92.89517

# Percent deviance explained

We can compute deviance residuals and percent-deviance explained:

    # Percent deviance explained
    out$deviance_explained
    #> [1] 0.5051624

We can then compare this with the PDE reported by `mgcv`

    start_time = Sys.time()
    mygam = gam( n ~ s(w) + s(x,y), data=Data ) #
    Sys.time() - start_time
    #> Time difference of 0.05233908 secs
    summary(mygam)$dev.expl
    #> [1] 0.3517756

where this comparison shows that using the SPDE method in tinyVAST results in higher percent-deviance-explained. This reduced performance for splines relative to the SPDE method presumably arises due to the reduced rank of the spline basis expansion, and the better match for the Matern function (in the SPDE method) relative to the true (simulated) exponential semivariogram.

It is then easy to confirm that mgcv and tinyVAST give (essentially) identical PDE when switching tinyVAST to use the same bivariate spline for space.

    out_reduced = tinyVAST( data = Data,
                            formula = n ~ s(w) + s(x,y) )

    # Extract PDE for GAM-style spatial smoother in tinyVAST
    out_reduced$deviance_explained
    #> [1] 0.3497174

# Visualize spatial response

`tinyVAST` then has a standard `predict` function:

    predict(out, newdata=data.frame(x=1, y=1, time=1, w=1, var="density") )
    #> [1] 0.3649899

and this is used to compute the spatial response

    # Prediction grid
    pred = outer( seq(1,n_x,len=51),
                  seq(1,n_y,len=51),
                  FUN=\(x,y) predict(out,newdata=data.frame(x=x,y=y,w=1,time=1,var="density")) )
    image( x=seq(1,n_x,len=51), y=seq(1,n_y,len=51), z=pred, main="Predicted response" )

    # True value
    image( x=1:n_x, y=1:n_y, z=matrix(Data$z,ncol=n_y), main="True response" )

We can also compute the marginal effect of the cyclic confounder

    # compute partial dependence plot
    Partial = partial( object = out,
                       pred.var = "w",
                       pred.fun = \(object,newdata) predict(object,newdata),
                       train = Data,
                       approx = TRUE )

    # Lattice plots as default option
    plotPartial( Partial )

Alternatively, we can use the `predict` function to plot confidence intervals for marginal effects, although this is disabled on CRAN vignettes:

    # create new data frame
    newdata <- data.frame(w = seq(min(Data$w), max(Data$w), length.out = 100))
    newdata = cbind( newdata, 'x'=13, 'y'=13, 'var'='density', 'time'=2020 )

    # make predictions
    p <- predict( out, newdata=newdata, se.fit=TRUE, what="p_g" )

    # Format as data frame and plot
    p = data.frame( newdata, as.data.frame(p) )
    ggplot(p, aes(x=w, y=fit,
      ymin = fit - 1.96 * se.fit, ymax = fit + 1.96 * se.fit)) +
      geom_line() + geom_ribbon(alpha = 0.4)

Runtime for this vignette: 2.74 secs