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This is pretty experimental, but the idea is to store matrix elements (which may be constants or expressions) in a long data format, then use a couple functions to take a user- or database supplied population vector and generate a density dependent matrix. Outputs are then generated via iteration.

These functions depend on rlang to work. rlang itself has no further dependencies, so this would be a fairly lightweight addition to Rcompadre/popdemo. Unfortunately, they do make use of env_bind_lazy() which is currently listed as experimental in the rlang lifecycle. If this is dropped in subsequent versions, we'll need to implement the delayed assignment manually.

These are now implemented. iterate_dd_mat() can handle both user-supplied and data base matrices.

CompadreDDM matrices do not look like other matrices stored in Compadre. They are lists with 2 elements: a data_list which contains values for each parameter and a mat_exprs list which contains expressions to calculate density dependent vital rates (e.g. survival, growth, reproduction). Each of these can accept any number of named values and has one additional required argument. data_list requires a named vector called initial_population_vector and the mat_exprs list requiress an expression for the matrix (mat_expr). Hopefully, the example below clarifies how these functions work.

To-do: incorporate stringToMatrix from Rcompadre so entering the matrices is easier on Compadrinos

# This is not yet part of the Rcompadre package, so you'll need source()
# the functions

source('R/functions.R')

# create expressions for each vital rate in the matrix using make_mat_exprs()

exprs <- make_mat_exprs(
  s_2 = 1/(1 + exp(bs2_2 * u_i + bs2_1 * t_i + bs2_0)),
  s_3 = exp(bs3_1 * log(r + 1)),
  f = exp(bf_1 * a + bf_0),
  u_i = r * a,
  t_i = r + a,
  mat_expr =
    matrix(
      c(
        1 - g_2, 0, v * (1-g_1) * f,
        g_2 * s_1, 0, v * g_1 * s_1 * f,
        0, s_2 * s_3, 0
      ),
      nrow = 3,
      byrow = TRUE

  )
)

# Use make_data_list() for constants and the initial population vector

data <- make_data_list(
  v = 0.8228,
  g_1 = 0.5503,
  g_2 = 0.3171,
  bs2_2 = 0.0016,
  bs2_1 = -0.0664,
  bs2_0 = -0.156,
  bs3_1 = -0.289,
  bf_1 = -0.0389,
  bf_0 = 7.489,
  s_1 = 0.5,
  initial_population_vector = c(s = 10, r = 0, a = 0)
)

Note that all constants in the mat_exprs list appear in the data_list. This includes coefficients in the density dependent expressions (bs2_0, bs2_1, bs2_2, bs3_0, bs3_1 bf_0, bf_1).

If you are interested in exploring how altering constant vital rates changes the way density dependence plays out, you can loop across the make_data_list() call and substitute in new values. You can do the same thing for the coefficients in each density dependent expression.

source('R/functions.R')

# make this outside the loop, the expressions are not changing
exprs <- make_mat_exprs(
  s_2 = 1/(1 + exp(bs2_2 * u_i + bs2_1 * t_i + bs2_0)),
  s_3 = exp(bs3_1 * log(r + 1)),
  f = exp(bf_1 * a + bf_0),
  u_i = r * a,
  t_i = r + a,
  mat_expr =
    matrix(
      c(
        1 - g_2, 0, v * (1-g_1) * f,
        g_2 * s_1, 0, v * g_1 * s_1 * f,
        0, s_2 * s_3, 0
      ),
      nrow = 3,
      byrow = TRUE

  )
)

# alter s_1 to see how this changes density dependent dynamics
lambdas <- list()
densities <- list()

for(i in seq(0.01, 1, 0.05)) {
  data <- make_data_list(
    v = 0.8228,
    g_1 = 0.5503,
    g_2 = 0.3171,
    bs2_2 = 0.0016,
    bs2_1 = -0.0664,
    bs2_0 = -0.156,
    bs3_1 = -0.289,
    bf_1 = -0.0389,
    bf_0 = 7.489,
    s_1 = i, # note the substitution here
    initial_population_vector = c(s = 10, r = 0, a = 0)
  )
  
  temp_data <- iterate_dd_mat(
    data_list = data,
    mat_exprs = exprs,
    n_generations = 100
  )
  
  lambdas[[paste0('s_1_', i, sep = "")]] <- temp_data$growth_rates$lambda
  densities[[paste0('s_1_', i, sep = "")]] <- apply(temp_data$stage_vectors[ ,2:3],
                                                    1, 
                                                    FUN = sum)
}

# plot the densities. You can change "densities" to "lambdas" in the code
# below to examine growth rates between each iteration
par(mfrow = c(4,5))

lapply(1:20, function(x) {
  plot(densities[[x]], 
       main = names(densities)[x],
       type = 'l')
  
  })

Iain Stott already has an implementation of these in popdemo. His implementation covers instances where multiple matrices are parameterized with constant values and then are selected either randomly, with Markov Chains, or a user-supplied sequence. I'm working on integrating the density dependent code into the Projection class that he wrote for these so that there is a common-ish feel to working with these stochastic matrices.

Still to be implemented are matrices where individual elements are functions of some environmental variable. These will be very similar to the density-dependent matrices, but I'm toying with the idea of adding an additional env_data list to the structure. Unfortunately, I have yet to find a paper that reports all of the parameters I need to re-create them, so I'm putting this on hold for now to focus on getting the density dependent matrices working across all/as many cases as possible.