Look into waywiser compatibility

[seananderson]

https://github.com/ropensci/waywiser

Suggested by @kellijohnson-NOAA

[ericward-noaa]

'waywiser' seems potentially useful, and I put together the example below (based on their vignette). The key steps here are:
1. Users need to select some kind of test/train split

• we already have this implemented in sdmTMB_cv

2. pass the object to waywiser

• I wrote a little function below that automates most of the sf stuff for this

library(sdmTMB)
library(waywiser)
library(sf)
# Build a mesh to implement the SPDE approach:
# Fit a Tweedie spatial random field GLMM with a smoother for depth:
data("pcod_2011")

mesh <- make_mesh(pcod, c("X", "Y"), cutoff = 25)

# use random folds (n = 8) but can modify
m_cv <- sdmTMB_cv(
  density ~ 0 + depth_scaled + depth_scaled2,
  data = pcod, mesh = mesh,
  family = tweedie()
)

# function to take a sdmTMB_cv object and convert to sf for waywiser
cv_to_waywiser <- function(object, ll_names = c("lon", "lat")) {
  formulas <- formula(object$models[[1]])
  resp_var <- all.vars(formulas)[1] # non-delta
  if(class(formulas) == "list") resp_var <- all.vars(formulas[[1]])[1] # delta
  
  point_df <- data.frame(
    truth = object$data[[resp_var]],
    estimate = object$data$cv_predicted,
    X = object$data$X,
    Y = object$data$Y
  )
  # convert to sf
  points <- sf::st_as_sf(point_df, 
                         coords = object$models[[1]]$spde$xy_cols, 
                         crs = get_crs(pcod_2011[, ll_names], 
                                            ll_names = ll_names))
  return(points)
}

pcod_points <- cv_to_waywiser(m_cv, ll_names = c("lon","lat"))

pcod_multi_scale <- ww_multi_scale(
  pcod_points,
  truth,# name of column, can be unquoted
  estimate,# name of column, can be unquoted
  n = list(c(5, 5), c(2, 2))
)