tests/testthat/test-invade3_basic.R

In adapt3: Adaptive Dynamics and Community Matrix Model Projections

test_that("invade3() basic example works", {
  library(lefko3)
  data(cypdata)
  
  sizevector <- c(0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
  stagevector <- c("SD", "P1", "SL", "D", "XSm", "Sm", "Md", "Lg", "XLg")
  repvector <- c(0, 0, 0, 0, 1, 1, 1, 1, 1)
  obsvector <- c(0, 0, 0, 0, 1, 1, 1, 1, 1)
  matvector <- c(0, 0, 0, 1, 1, 1, 1, 1, 1)
  immvector <- c(0, 1, 1, 0, 0, 0, 0, 0, 0)
  propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0)
  indataset <- c(0, 0, 0, 1, 1, 1, 1, 1, 1)
  binvec <- c(0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
  
  cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
    repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
    propstatus = propvector, immstatus = immvector, indataset = indataset,
    binhalfwidth = binvec)
  
  cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
    patchidcol = "patch", individcol = "plantid", blocksize = 4,
    sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
    repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
    stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
    NRasRep = TRUE)
  
  cypsupp2r <- supplemental(stage3 = c("SD", "P1", "SL", "D", 
      "XSm", "Sm", "SD", "P1"),
    stage2 = c("SD", "SD", "P1", "SL", "SL", "SL", "rep",
      "rep"),
    eststage3 = c(NA, NA, NA, "D", "XSm", "Sm", NA, NA),
    eststage2 = c(NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
    givenrate = c(0.10, 0.40, 0.25, NA, NA, NA, NA, NA),
    multiplier = c(1, 1, 1, 1, 1, 1, 1000, 1000),
    type =c(1, 1, 1, 1, 1, 1, 3, 3),
    stageframe = cypframe_raw, historical = FALSE)
  
  cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw, 
    year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
    size = c("size3added", "size2added"), supplement = cypsupp2r,
    yearcol = "year2", patchcol = "patchid", indivcol = "individ")
  cypmean <- lmean(cypmatrix2r)
  
  cyp_start <- start_input(cypmean, stage2 = c("SD", "P1", "D"),
    value = c(1000, 200, 4))
  
  c2d_4 <- density_input(cypmean, stage3 = c("P1", "P1"), stage2= c("SD", "rep"),
    style = 2, time_delay = 1, alpha = 0.005, beta = 0.000005, type = c(2, 2))
  
  # A simple projection allows us to find a combination of density dependence
  # and running time that produces a stable quasi-equilibrium
  suppressWarnings(cyp_proj <- projection3(cypmean, times = 250, start_frame = cyp_start,
    density = c2d_4, integeronly = TRUE))
  plot(cyp_proj)
  
  cyp_ta <- trait_axis(stageframe = cypframe_raw,
    stage3 = rep("P1", 15),
    stage2 = rep("rep", 15),
    multiplier = seq(from = 0.1, to = 10.0, length.out = 15),
    type = rep(2, 15))
  
  cyp_inv <- invade3(axis = cyp_ta, mpm = cypmean, density = c2d_4, times = 350,
    starts = cyp_start, entry_time = c(0, 250), fitness_times = 30,
    var_per_run = 2, trait_optima = TRUE)
  
  expect_true(class(cyp_inv) == "adaptInv")
  expect_true(length(cyp_inv$fitness$fitness_variant2) > 0)
})