Nothing
R/internal-model_iteration.R
In ipmr: Integral Projection Models
Defines functions
.bind_iter_var
.iterate_model.general_dd_stoch_param
.iterate_model.general_dd_stoch_kern
.iterate_model.general_dd_det
.iterate_model.simple_dd_stoch_param
.iterate_model.simple_dd_stoch_kern
.iterate_model.simple_dd_det
.iterate_model.general_di_stoch_param
.iterate_model.general_di_stoch_kern
.iterate_model.general_di_det
.iterate_model.simple_di_stoch_param
.iterate_model.simple_di_stoch_kern
.iterate_model.simple_di_det
.iterate_model
#' @noRd
.iterate_model <- function(proto_ipm, ...) {
UseMethod('.iterate_model')
}
#' @noRd
#' @importFrom purrr map2
.iterate_model.simple_di_det <- function(proto_ipm,
sub_kern_list,
iterations,
pop_state,
main_env,
k_row,
normal) {
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
for(i in seq_along(levs)) {
use_lev <- paste("(_", levs[i], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
for(j in seq_len(iterations)) {
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, j)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, j)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, iter = j)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # end single level simulation
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
for(i in seq_len(iterations)) {
# Simple model, no par_set stuff going on. We can just
# stick the kernels and their expressions right into eval_general_det
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, iter = i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End no par_set stuff
} # End if(par_set){} else {}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
return(pop_state)
}
#' @noRd
.iterate_model.simple_di_stoch_kern <- function(proto_ipm,
sub_kern_list,
iterations,
kern_seq,
pop_state,
main_env,
k_row,
normal,
report_progress) {
if(rlang::is_quosure(pop_state)) {
pop_state <- rlang::eval_tidy(pop_state)
}
for(i in seq_len(iterations)) {
# Select kernels from kern_seq. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, iter = i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
return(pop_state)
}
#' @noRd
.iterate_model.simple_di_stoch_param <- function(proto_ipm,
sub_kern_list,
current_iteration,
kern_seq,
pop_state,
main_env,
k_row,
normal) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state,
pop_list_t_1,
iter = current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_di_det <- function(proto_ipm,
k_row,
sub_kern_list,
iterations,
pop_state,
main_env,
normal) {
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
for(i in seq_along(levs)) {
use_lev <- paste("(_", levs[i], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
for(j in seq_len(iterations)) {
.bind_iter_var(main_env, j)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, j)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, j)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # end single level simulation
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
for(i in seq_len(iterations)) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
use_kerns <- sub_kern_list
use_k <- k_row
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
return(pop_state)
}
#' @noRd
.iterate_model.general_di_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
for(i in seq_len(iterations)) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
}
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
return(pop_state)
}
#' @noRd
.iterate_model.general_di_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
kern_seq,
pop_state,
main_env,
normal) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.simple_dd_det <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
pop_state,
main_env,
normal) {
i <- current_iteration
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
# Set up altered lambda list names if first iteration
if(i == 1) {
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
}
# otherwise, just iterate the model
for(j in seq_along(levs)) {
use_lev <- paste("(_", levs[j], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End if(par_setarchical){} else {}
return(pop_state)
}
#' @noRd
.iterate_model.simple_dd_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
i <- current_iteration
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
stop("something went wrong!")
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
.iterate_model.simple_dd_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_det <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
pop_state,
main_env,
normal) {
i <- current_iteration
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
# Set up altered lambda list names if first iteration
if(i == 1) {
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
}
# otherwise, just iterate the model
for(j in seq_along(levs)) {
use_lev <- paste("(_", levs[j], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End if(par_setarchical){} else {}
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
i <- current_iteration
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
stop("something went wrong!")
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state,
pop_list_t_1,
current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
# Helpers --------------
#' @noRd
.bind_iter_var <- function(env, it) {
temp <- list(t = it)
rlang::env_bind(env,
!!! temp)
invisible(TRUE)
}
Try the ipmr package in your browser
Any scripts or data that you put into this service are public.
ipmr documentation built on Feb. 16, 2023, 10:04 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions .bind_iter_var .iterate_model.general_dd_stoch_param .iterate_model.general_dd_stoch_kern .iterate_model.general_dd_det .iterate_model.simple_dd_stoch_param .iterate_model.simple_dd_stoch_kern .iterate_model.simple_dd_det .iterate_model.general_di_stoch_param .iterate_model.general_di_stoch_kern .iterate_model.general_di_det .iterate_model.simple_di_stoch_param .iterate_model.simple_di_stoch_kern .iterate_model.simple_di_det .iterate_model
#' @noRd
.iterate_model <- function(proto_ipm, ...) {
UseMethod('.iterate_model')
}
#' @noRd
#' @importFrom purrr map2
.iterate_model.simple_di_det <- function(proto_ipm,
sub_kern_list,
iterations,
pop_state,
main_env,
k_row,
normal) {
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
for(i in seq_along(levs)) {
use_lev <- paste("(_", levs[i], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
for(j in seq_len(iterations)) {
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, j)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, j)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, iter = j)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # end single level simulation
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
for(i in seq_len(iterations)) {
# Simple model, no par_set stuff going on. We can just
# stick the kernels and their expressions right into eval_general_det
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, iter = i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End no par_set stuff
} # End if(par_set){} else {}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
return(pop_state)
}
#' @noRd
.iterate_model.simple_di_stoch_kern <- function(proto_ipm,
sub_kern_list,
iterations,
kern_seq,
pop_state,
main_env,
k_row,
normal,
report_progress) {
if(rlang::is_quosure(pop_state)) {
pop_state <- rlang::eval_tidy(pop_state)
}
for(i in seq_len(iterations)) {
# Select kernels from kern_seq. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, iter = i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
return(pop_state)
}
#' @noRd
.iterate_model.simple_di_stoch_param <- function(proto_ipm,
sub_kern_list,
current_iteration,
kern_seq,
pop_state,
main_env,
k_row,
normal) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state,
pop_list_t_1,
iter = current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_di_det <- function(proto_ipm,
k_row,
sub_kern_list,
iterations,
pop_state,
main_env,
normal) {
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
for(i in seq_along(levs)) {
use_lev <- paste("(_", levs[i], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
for(j in seq_len(iterations)) {
.bind_iter_var(main_env, j)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, j)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, j)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # end single level simulation
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
for(i in seq_len(iterations)) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
use_kerns <- sub_kern_list
use_k <- k_row
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
return(pop_state)
}
#' @noRd
.iterate_model.general_di_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
for(i in seq_len(iterations)) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
}
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
}
return(pop_state)
}
#' @noRd
.iterate_model.general_di_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
kern_seq,
pop_state,
main_env,
normal) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.simple_dd_det <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
pop_state,
main_env,
normal) {
i <- current_iteration
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
# Set up altered lambda list names if first iteration
if(i == 1) {
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
}
# otherwise, just iterate the model
for(j in seq_along(levs)) {
use_lev <- paste("(_", levs[j], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End if(par_setarchical){} else {}
return(pop_state)
}
#' @noRd
.iterate_model.simple_dd_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
i <- current_iteration
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
stop("something went wrong!")
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
.iterate_model.simple_dd_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_det <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
pop_state,
main_env,
normal) {
i <- current_iteration
# For par_setarchical deterministic models, we need to create a determinstic
# simulation for each level of the model
if(any(proto_ipm$uses_par_sets)) {
par_sets <- proto_ipm$par_set_indices[proto_ipm$uses_par_sets]
par_sets <- par_sets[!duplicated(par_sets)]
levs <- .make_par_set_indices(par_sets)
# Set up altered lambda list names if first iteration
if(i == 1) {
lambda_nms <- paste("lambda", levs, sep = "_")
pop_state$lambda <- NULL
lambdas <- vector('list', length = length(levs))
names(lambdas) <- lambda_nms
lambdas <- lapply(lambdas,
function(x, iterations) {
x <- array(NA_real_, dim = c(1, (iterations + 1)))
},
iterations = iterations)
pop_state <- c(pop_state, lambdas)
}
# otherwise, just iterate the model
for(j in seq_along(levs)) {
use_lev <- paste("(_", levs[j], ")$", sep = "")
use_kerns <- sub_kern_list[grepl(use_lev, names(sub_kern_list))]
if(!all(proto_ipm$uses_par_sets)) {
append <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
use_kerns <- c(use_kerns, sub_kern_list[append])
}
use_pop <- pop_state[grepl(use_lev, names(pop_state))]
use_k <- k_row[grepl(use_lev, k_row$kernel_id), ]
# Bind a helper for the model iteration number. Users can use this
# to specify lagged effects.
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(use_pop, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
use_l_nm <- names(use_pop)[grepl("lambda", names(use_pop))]
names(pop_list_t_1)[names(pop_list_t_1) == 'lambda'] <- use_l_nm
pop_list_t_1 <- pop_list_t_1[names(use_pop)]
use_pop <- update_pop_state(use_pop, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
pop_state[grepl(use_lev, names(pop_state))] <- use_pop
} # end par_setarchical levels loop
} else {
.bind_iter_var(main_env, i)
pop_list_t_1 <- .eval_general_det(k_row = k_row,
proto_ipm = proto_ipm,
sub_kern_list = sub_kern_list,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
} # End if(par_setarchical){} else {}
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_stoch_kern <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
i <- current_iteration
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[i], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[i], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
stop("something went wrong!")
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, i)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state, pop_list_t_1, i)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
#' @noRd
.iterate_model.general_dd_stoch_param <- function(proto_ipm,
k_row,
sub_kern_list,
current_iteration,
iterations,
kern_seq,
pop_state,
main_env,
normal,
report_progress) {
# Select kernels from kern_seq, or just use all of them if it's
# a deterministic simulation. Similarly, we need to subset the k_rows
# object so that .eval_general_det doesn't loop over ones which include
# expressions for other levels of 'kern_seq'
if(!is.null(kern_seq)) {
if(is.character(kern_seq)) {
kern_ind <- grepl(kern_seq[current_iteration], names(sub_kern_list))
k_row_ind <- which(grepl(kern_seq[current_iteration], k_row$kernel_id))
use_kerns <- sub_kern_list[kern_ind]
use_k <- k_row[k_row_ind, ]
# Deals with the case where only a subset of kernels have suffixes.
# In that case, we need to include the ones that don't have them
# in the use_kerns list every single time!
if(any(!proto_ipm$uses_par_sets) && any(proto_ipm$uses_par_sets)) {
nm_ind <- proto_ipm$kernel_id[!proto_ipm$uses_par_sets]
to_add <- sub_kern_list[nm_ind]
use_kerns <- c(use_kerns, to_add)
}
}
} else {
use_kerns <- sub_kern_list
use_k <- k_row
}
# Need to return an iterated pop_state object
pop_list_t_1 <- .eval_general_det(k_row = use_k,
proto_ipm = proto_ipm,
sub_kern_list = use_kerns,
main_env = main_env)
# make names match pop_state and then reorder the list for easy
# insertion
names(pop_list_t_1) <- gsub('_t_1', '', names(pop_list_t_1))
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
# Drop the NA entry created by the lambda slot.
pop_list_t_1 <- pop_list_t_1[!is.na(names(pop_list_t_1))]
# Next, compute population sizes and time step growth rates.
# this is required because if normalize_pop_size = TRUE,
# there isn't any way to compute growth rates at each
# time step after the fact
pop_size_t_1 <- vapply(
pop_list_t_1,
function(x) sum(x),
numeric(1L)
) %>%
Reduce(f = '+', x = ., init = 0)
pop_size_t <- .pop_size(pop_state, current_iteration)
if(normal) {
pop_list_t_1 <- lapply(pop_list_t_1,
function(x, pop_size) {
x / pop_size
},
pop_size = pop_size_t_1)
}
pop_list_t_1$lambda <- pop_size_t_1 / pop_size_t
pop_list_t_1 <- pop_list_t_1[names(pop_state)]
pop_state <- update_pop_state(pop_state,
pop_list_t_1,
current_iteration)
# Now, update the names so that we can bind the new n_*_t to
# main_env so that the next iteration is evaluated correctly
names(pop_list_t_1) <- names(pop_list_t_1) %>%
paste(., '_t', sep = "")
rlang::env_bind(.env = main_env,
!!! pop_list_t_1)
return(pop_state)
}
# Helpers --------------
#' @noRd
.bind_iter_var <- function(env, it) {
temp <- list(t = it)
rlang::env_bind(env,
!!! temp)
invisible(TRUE)
}
Try the ipmr package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.