Nothing
## ----eval = FALSE-------------------------------------------------------------
#
# library(ipmr)
#
# data_list = list(
# s_int = 1.03,
# s_slope = 2.2,
# s_dd = -0.7,
# g_int = 8,
# g_slope = 0.92,
# sd_g = 0.9,
# r_r_int = 0.09,
# r_r_slope = 0.05,
# r_s_int = 0.1,
# r_s_slope = 0.005,
# r_s_dd = -0.03,
# mu_rd = 9,
# sd_rd = 2
# )
#
# # Now, simulate some random intercepts for growth, survival, and offspring production
#
# g_r_int <- rnorm(5, 0, 0.3)
# s_r_int <- rnorm(5, 0, 0.7)
# r_s_r_int <- rnorm(5, 0, 0.2)
#
# nms <- paste("r_", 1:5, sep = "")
#
# names(g_r_int) <- paste("g_", nms, sep = "")
# names(s_r_int) <- paste("s_", nms, sep = "")
# names(r_s_r_int) <- paste("r_s_", nms, sep = "")
#
# params <- c(data_list, g_r_int, s_r_int, r_s_r_int)
#
## ----eval = FALSE-------------------------------------------------------------
#
# dd_ipm <- init_ipm(sim_gen = "simple",
# di_dd = "dd",
# det_stoch = "stoch",
# kern_param = "kern")
## ----eval = FALSE-------------------------------------------------------------
# dd_ipm <- define_kernel(
# proto_ipm = dd_ipm,
# name = "P_yr",
# formula = s_yr * g_yr,
# family = "CC",
# s_yr = plogis(s_int + s_r_yr + s_slope * size_1 + s_dd * sum(n_size_t)),
# g_yr = dnorm(size_2, g_mu_yr, sd_g),
# g_mu_yr = g_int + g_r_yr + g_slope * size_1,
# data_list = params,
# states = list(c("size")),
# uses_par_sets = TRUE,
# par_set_indices = list(yr = 1:5),
# evict_cor = TRUE,
# evict_fun = truncated_distributions("norm", "g_yr")
# )
#
## ----eval = FALSE-------------------------------------------------------------
#
# dd_ipm <- define_kernel(
# proto_ipm = dd_ipm,
# name = "F_yr",
# formula = r_r * r_s_yr * r_d,
# family = "CC",
# r_r = plogis(r_r_int + r_r_slope * size_1),
# r_s_yr = exp(r_s_int + r_s_r_yr + r_s_slope * size_1 + r_s_dd * sum(n_size_t)),
# r_d = dnorm(size_2, mu_rd, sd_rd),
# data_list = params,
# states = list(c("size")),
# uses_par_sets = TRUE,
# par_set_indices = list(yr = 1:5),
# evict_cor = TRUE,
# evict_fun = truncated_distributions("norm", "r_d")
# )
#
## ----eval = FALSE-------------------------------------------------------------
# dd_ipm <- dd_ipm %>%
# define_impl(
# make_impl_args_list(
# kernel_names = c("P_yr", "F_yr"),
# int_rule = rep("midpoint", 2),
# state_start = rep("size", 2),
# state_end = rep("size", 2)
# )
# ) %>%
# define_domains(
# size = c(0, 50, 200)
# ) %>%
# define_pop_state(
# n_size = runif(200)
# ) %>%
# make_ipm(
# iterate = TRUE,
# iterations = 50,
# kernel_seq = sample(1:5, 50, replace = TRUE)
# )
#
#
## ----eval = FALSE-------------------------------------------------------------
# time_step_lams <- lambda(dd_ipm, type_lambda = "all")
# stoch_lam <- lambda(dd_ipm, type_lambda = "stochastic", burn_in = 0.15)
#
# pop_sizes <- colSums(dd_ipm$pop_state$n_size)
#
# plot(pop_sizes, type = "l")
#
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