In andrewmarx/samc: Spatial Absorbing Markov Chains

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Background

The samc package currently supports a random-walk (RW) model. The random-walk is the default model that always has been used by the package. Version 3 made breaking changes to the samc() function to setup a structure for specifying different models, with plans for a correlated random-walk model in the future.

Models

Models in the samc package are defined as a list with various components depending on the context:

list(name, fun, dir, sym)

Random-Walk

The basic random-walk is the default model used by the package and uses the following properties:

name: can be omitted or set to "RW"
fun: a function for calculating transition probabilities from the data input. The convolution algorithm does not allow for custom functions, so this should be omitted in that context.
dir can be either 4 or 8
sym can be either TRUE or FALSE, and is used as an optimization when calculating transition probabilities.

Examples

library("terra")
library("samc")
library("viridisLite")

res_data <- samc::rasterize(example_toy_res)
abs_data <- samc::rasterize(example_toy_res * 0 + 0.05)

plot(res_data, main = "Resistance")

rw_model <- list(fun = "1/mean(x)", dir = 8, sym = TRUE)

samc_rw <- samc(res_data, abs_data, model = rw_model)

origin = 85 # Centered near the bottom
dir = 1 # Up and left

vis_rw <- as.vector(visitation(samc_rw, origin = origin))

# The RW results
plot(map(samc_rw, vis_rw), col = viridis(1024), main = "RW")