Nothing
R/make_ipm.R
In ipmr: Integral Projection Models
Defines functions
make_ipm.general_dd_stoch_param
make_ipm.general_dd_stoch_kern
make_ipm.general_dd_det
make_ipm.simple_dd_stoch_param
make_ipm.simple_dd_stoch_kern
make_ipm.simple_dd_det
make_ipm.general_di_stoch_param
make_ipm.general_di_stoch_kern
make_ipm.general_di_det
make_ipm.simple_di_stoch_param
make_ipm.simple_di_stoch_kern
make_ipm.simple_di_det
make_ipm
Documented in
make_ipm
make_ipm.general_dd_det
make_ipm.general_dd_stoch_kern
make_ipm.general_dd_stoch_param
make_ipm.general_di_det
make_ipm.general_di_stoch_kern
make_ipm.general_di_stoch_param
make_ipm.simple_dd_det
make_ipm.simple_dd_stoch_kern
make_ipm.simple_dd_stoch_param
make_ipm.simple_di_det
make_ipm.simple_di_stoch_kern
make_ipm.simple_di_stoch_param
#' @title Methods to implement an IPM
#' @rdname make_ipm
#'
#' @description The \code{make_ipm.*} methods convert a \code{proto_ipm} into a
#' set of discretized kernels and population vectors. Methods have different
#' requirements, so be sure to read the parameter documentation. \code{
#' vignette('ipmr-introduction', 'ipmr')} a more complete introduction.
#'
#' @param proto_ipm A proto_ipm. This should be the
#' output of \code{define_kernel}, or the \code{define_*} functions.
#'
#' @param ... Other arguments passed to methods.
#'
#' @param return_main_env A logical indicating whether to return the main environment
#' for the model. This environment contains the integration mesh, weights, and
#' other potentially useful variables for subsequent analyses. Default is
#' \code{TRUE}.
#'
#' @param return_all_envs A logical indicating whether to return the environments that
#' the kernel expressions are evaluated in. These may be useful for some analyses,
#' such as regression-level sensitivity/elasticity analyses, but can also rapidly
#' increase memory consumption for models with many kernels (e.g. ones with
#' parameter set indices that have many levels, or any \code{*_stoch_param} model).
#' Default is \code{FALSE}.
#'
#' @param domain_list An optional list of new domain information to implement
#' the IPM with.
#'
#' @param usr_funs An optional list of user-specified functions that are passed
#' on to the model building process. This can help make vital rate expressions
#' more concise and expressive. Names in this list should exactly match the names
#' of the function calls in the \code{...} or \code{formula}.
#'
#' @param iterate A logical indicating whether or not iterate the model before exiting
#' or just return the sub-kernels. Only applies to density-independent, deterministic
#' models and density-independent, stochastic kernel re-sampled models.
#'
#' @param iterations If \code{iterate} is \code{TRUE}, then the number of iterations
#' to run the model for.
#'
#' @param normalize_pop_size A logical indicating whether to re-scale the population
#' vector to sum to 1 before each iteration. Default is \code{TRUE} for
#' \code{*_di_*} methods and \code{FALSE} for \code{*_dd_*} methods.
#'
#' @param kernel_seq For \code{*_stoch_kern} methods, the sequence of kernels
#' to use during the simulation process. It should have the same number of entries
#' as the number of \code{iterations}.
#' This should be a vector containing values of the parameter set indices specified
#' in \code{par_set_indices}, or empty. Support for Markov chains will eventually
#' get implemented. If it is empty, \code{make_ipm} will try to generate a
#' sequence internally using a random selection of the \code{par_set_indices}
#' defined in \code{define_kernel}.
#'
#' @param report_progress A logical indicating whether or not to periodically
#' report progress for a stochastic simulation. Does not apply to deterministic
#' methods. Default is \code{FALSE}.
#'
#' @param iteration_direction Either \code{"right"} (default) or \code{"left"}.
#' This controls the direction of projection. Right iteration will generate
#' the right eigenvector (if it exists), while left iteration generates
#' the left eigenvector. These correspond to the stable trait distributions, and
#' reproductive values, respectively. This parameter is mostly used internally
#' by other functions. Use with care.
#'
#' @param return_sub_kernels Only applies to density dependent and parameter
#' resampled models. If \code{TRUE}, then all sub-kernels will be returned. These
#' are required for some analyses, but a large number of iterations will
#' take up lots of RAM. Default is \code{FALSE}.
#'
#' @return
#' The \code{make_ipm.*} methods will always return a list of length 5
#' containing the following components:
#'
#' \itemize{
#' \item{\strong{sub_kernels}}{: a list of arrays specified in \code{define_kernel}.}
#' \item{\strong{env_list}}{: a list containing the evaluation environments of
#' kernel. This will contain the \code{main_env} object
#' if \code{return_main_env = TRUE}. It will also contain
#' the sub-kernels evaluation environments if
#' \code{return_all_envs = TRUE}. }
#' \item{\strong{env_seq}}{: a character vector with length \code{iterations} of
#' kernel indices indicating the order
#' in which kernels are to be/were resampled OR
#' a matrix with as many columns as stochastic parameters
#' and \code{n_iterations} rows.}
#' \item{\strong{pop_state}}{: population vectors
#' stored as a list of arrays. The first dimension
#' of each array corresponds to the state variable distribution,
#' and the second dimension corresponds to time
#' steps.}
#' \item{\strong{proto_ipm}}{: the \code{proto_ipm} object used to implement
#' the model.}
#' }
#'
#' In addition to the list class, each object will have a class comprised of the
#' arguments from \code{init_ipm} plus \code{'ipm'} pasted together with
#' underscores. This is to facilitate \code{print}, \code{plot}, and
#' \code{lambda} methods. For example, a \code{init_ipm("general", "di", "det")}
#' model will have the class \code{'general_di_det_ipm'} once it has been
#' implemented using \code{make_ipm}.
#'
#' @export
make_ipm <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...) {
UseMethod('make_ipm')
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = TRUE,
iteration_direction = "right"
) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function definitions
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_simple(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
if(iterate) {
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kern_list,
iterations,
temp$pop_state,
main_env,
k_row,
normalize_pop_size)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- all_sub_kerns$env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
out_seq <- NA_integer_
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(sub_kernels = sub_kern_list,
env_list = out_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_det_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right") {
if(iterate && all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# checks pop_state, env_state, domain_definitions
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)){
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
# construct the kernels from their function defintions
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_simple(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
if(iterate) {
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kern_list,
iterations,
kern_seq,
temp$pop_state,
main_env,
k_row,
normalize_pop_size,
report_progress)
# In order to operate properly, we had to insert an extra entry in
# lambda to make sure it had the same length as pop_state (it'll always be
# one fewer in reality though). The first entry is ALWAYS NA, but that doesn't
# seem user friendly (ipmr-internal friend either, tbh). After that,
# we need to conver the names of the trait distributions back to the
# user-supplied ones
pop_state$lambda <- pop_state$lambda[-1]
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(sub_kernels = sub_kern_list,
env_list = out_ret,
env_seq = kern_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_stoch_kern_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_stoch_param <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# initialize the pop_state vectors in main_env so they can be found
# at evaluation time
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# list to hold the possibly returned evaluation environments
env_list <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = 'n')
sub_kernels <- sys$ipm_system$sub_kernels
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kernels,
current_iteration = i,
kern_seq = kern_seq,
temp$pop_state,
main_env,
k_row,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(!return_main_env) {
env_list <- NA_character_
}
temp <- .update_param_output(
sub_kernels,
pop_state,
env_list,
main_env,
temp,
iterations,
i,
return_sub_kernels
)
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
temp$pop_state$lambda <- temp$pop_state$lambda[-1]
names(temp$pop_state) <- gsub("^pop_state_", "n_", names(temp$pop_state))
out <- list(
sub_kernels = temp$sub_kernels,
env_list = env_list,
env_seq = temp$env_seq,
pop_state = temp$pop_state,
proto_ipm = proto_ipm
)
out$sub_kernels <- set_ipmr_classes(out$sub_kernels)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_stoch_param_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = TRUE,
iteration_direction = "right") {
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# initialize others + k_row
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain,
usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# Thus far, I think general_* methods will have to use iteration for lambdas
# as I'm not sure I want to work out the correct cbind(rbind(...)) rules for
# creating a mega-matrix. So throw an error if pop_state isn't defined
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_general(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
# Things switch up here from the simple_* versions. Rather than construct a mega-K
# through rbind + cbinding, we just iterate the population vector with the
# sub kernels. This means I don't have to overload all of the arithmetic
# operators and keeps things simpler internally. It also means there's no
# need to implement sparse kernels/matrices for things like an age x size IPM.
if(iterate) {
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kern_list,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
}
# Final bits of housekeeping post-iteration. .prep_other_output deals specifically
# with items that depend on `return_all_envs` and `iterate` switches, but have
# length > 1 (ifelse() output always equals length(input))
sub_kern_list <- set_ipmr_classes(sub_kern_list)
temp_other_out <- .prep_other_output(env_list,
kern_seq = NA_integer_,
pop_state,
return_main_env,
return_all_envs,
iterate)
env_ret <- temp_other_out$env_ret
env_seq_ret <- temp_other_out$env_seq_ret
pop_ret <- temp_other_out$pop_ret
out <- list(
sub_kernels = sub_kern_list,
env_list = env_ret,
env_seq = env_seq_ret,
pop_state = pop_ret,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_det_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right") {
if(iterate && all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# initialize others + k_row
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# Thus far, I think general_* methods will have to use iteration for lambdas
# as I'm not sure I want to work out the correct cbind(rbind(...)) rules for
# creating a mega-matrix. So throw an error if pop_state isn't defined
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_general(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
if(iterate) {
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kern_list,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
pop_state$lambda <- pop_state$lambda[-1]
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
}
# Final bits of housekeeping post-iteration. .prep_other_output deals specifically
# with items that depend on `return_all_envs` and `iterate` switches, but have
# length > 1 (ifelse() output always equals length(input))
temp_other_out <- .prep_other_output(env_list,
kern_seq,
pop_state,
return_main_env,
return_all_envs,
iterate)
env_ret <- temp_other_out$env_ret
env_seq_ret <- temp_other_out$env_seq_ret
pop_ret <- temp_other_out$pop_ret
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(
sub_kernels = sub_kern_list,
env_list = env_ret,
env_seq = env_seq_ret,
pop_state = pop_ret,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_stoch_kern_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_stoch_param <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repetition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# initialize the pop_state vectors in main_env so they can be found
# at evaluation time
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
if(!iterate || iterations < 1) {
stop("All 'general_*_stoch_param' models must be iterated at least once!",
call. = FALSE)
}
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
env_ret <- list()
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_general_lazy(others,
main_env,
return_envs = return_all_envs)
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration.
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
kern_seq = kern_seq,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$ipm_system$env_list$main_env <- NULL
names(sys$ipm_system$env_list) <- paste(names(sys$ipm_system$env_list),
"it", i, sep = "_")
env_ret <- c(env_ret, sys$ipm_system$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
temp$pop_state$lambda <- temp$pop_state$lambda[-1]
names(temp$pop_state) <- gsub("^pop_state_", "n_", names(temp$pop_state))
out <- list(
sub_kernels = temp$sub_kernels,
env_list = c(list(main_env = main_env), temp$sub_kernel_envs),
env_seq = temp$env_seq,
pop_state = temp$pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
out$sub_kernels <- set_ipmr_classes(out$sub_kernels)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_stoch_param_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
# Density dependent methods----------
#' @rdname make_ipm
#' @export
make_ipm.simple_dd_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# 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)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- NA_integer_
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_det_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_dd_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
temp$sub_kernels <- list()
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- kern_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_stoch_kern_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_dd_stoch_param <-function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "n")
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
names(sub_kernels) <- paste(names(sub_kernels),
"it",
i,
sep = "_")
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
out_seq <- temp$env_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_stoch_param_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE
) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
for(i in seq_len(iterations)) {
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# 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)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- temp$env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
out_seq <- NA_integer_
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_det_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- kern_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_stoch_kern_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_stoch_param <-function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "n")
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
temp$env_seq <- data.frame(temp$env_seq,
stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
out_seq <- temp$env_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_stoch_param_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
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Defines functions make_ipm.general_dd_stoch_param make_ipm.general_dd_stoch_kern make_ipm.general_dd_det make_ipm.simple_dd_stoch_param make_ipm.simple_dd_stoch_kern make_ipm.simple_dd_det make_ipm.general_di_stoch_param make_ipm.general_di_stoch_kern make_ipm.general_di_det make_ipm.simple_di_stoch_param make_ipm.simple_di_stoch_kern make_ipm.simple_di_det make_ipm
Documented in make_ipm make_ipm.general_dd_det make_ipm.general_dd_stoch_kern make_ipm.general_dd_stoch_param make_ipm.general_di_det make_ipm.general_di_stoch_kern make_ipm.general_di_stoch_param make_ipm.simple_dd_det make_ipm.simple_dd_stoch_kern make_ipm.simple_dd_stoch_param make_ipm.simple_di_det make_ipm.simple_di_stoch_kern make_ipm.simple_di_stoch_param
#' @title Methods to implement an IPM
#' @rdname make_ipm
#'
#' @description The \code{make_ipm.*} methods convert a \code{proto_ipm} into a
#' set of discretized kernels and population vectors. Methods have different
#' requirements, so be sure to read the parameter documentation. \code{
#' vignette('ipmr-introduction', 'ipmr')} a more complete introduction.
#'
#' @param proto_ipm A proto_ipm. This should be the
#' output of \code{define_kernel}, or the \code{define_*} functions.
#'
#' @param ... Other arguments passed to methods.
#'
#' @param return_main_env A logical indicating whether to return the main environment
#' for the model. This environment contains the integration mesh, weights, and
#' other potentially useful variables for subsequent analyses. Default is
#' \code{TRUE}.
#'
#' @param return_all_envs A logical indicating whether to return the environments that
#' the kernel expressions are evaluated in. These may be useful for some analyses,
#' such as regression-level sensitivity/elasticity analyses, but can also rapidly
#' increase memory consumption for models with many kernels (e.g. ones with
#' parameter set indices that have many levels, or any \code{*_stoch_param} model).
#' Default is \code{FALSE}.
#'
#' @param domain_list An optional list of new domain information to implement
#' the IPM with.
#'
#' @param usr_funs An optional list of user-specified functions that are passed
#' on to the model building process. This can help make vital rate expressions
#' more concise and expressive. Names in this list should exactly match the names
#' of the function calls in the \code{...} or \code{formula}.
#'
#' @param iterate A logical indicating whether or not iterate the model before exiting
#' or just return the sub-kernels. Only applies to density-independent, deterministic
#' models and density-independent, stochastic kernel re-sampled models.
#'
#' @param iterations If \code{iterate} is \code{TRUE}, then the number of iterations
#' to run the model for.
#'
#' @param normalize_pop_size A logical indicating whether to re-scale the population
#' vector to sum to 1 before each iteration. Default is \code{TRUE} for
#' \code{*_di_*} methods and \code{FALSE} for \code{*_dd_*} methods.
#'
#' @param kernel_seq For \code{*_stoch_kern} methods, the sequence of kernels
#' to use during the simulation process. It should have the same number of entries
#' as the number of \code{iterations}.
#' This should be a vector containing values of the parameter set indices specified
#' in \code{par_set_indices}, or empty. Support for Markov chains will eventually
#' get implemented. If it is empty, \code{make_ipm} will try to generate a
#' sequence internally using a random selection of the \code{par_set_indices}
#' defined in \code{define_kernel}.
#'
#' @param report_progress A logical indicating whether or not to periodically
#' report progress for a stochastic simulation. Does not apply to deterministic
#' methods. Default is \code{FALSE}.
#'
#' @param iteration_direction Either \code{"right"} (default) or \code{"left"}.
#' This controls the direction of projection. Right iteration will generate
#' the right eigenvector (if it exists), while left iteration generates
#' the left eigenvector. These correspond to the stable trait distributions, and
#' reproductive values, respectively. This parameter is mostly used internally
#' by other functions. Use with care.
#'
#' @param return_sub_kernels Only applies to density dependent and parameter
#' resampled models. If \code{TRUE}, then all sub-kernels will be returned. These
#' are required for some analyses, but a large number of iterations will
#' take up lots of RAM. Default is \code{FALSE}.
#'
#' @return
#' The \code{make_ipm.*} methods will always return a list of length 5
#' containing the following components:
#'
#' \itemize{
#' \item{\strong{sub_kernels}}{: a list of arrays specified in \code{define_kernel}.}
#' \item{\strong{env_list}}{: a list containing the evaluation environments of
#' kernel. This will contain the \code{main_env} object
#' if \code{return_main_env = TRUE}. It will also contain
#' the sub-kernels evaluation environments if
#' \code{return_all_envs = TRUE}. }
#' \item{\strong{env_seq}}{: a character vector with length \code{iterations} of
#' kernel indices indicating the order
#' in which kernels are to be/were resampled OR
#' a matrix with as many columns as stochastic parameters
#' and \code{n_iterations} rows.}
#' \item{\strong{pop_state}}{: population vectors
#' stored as a list of arrays. The first dimension
#' of each array corresponds to the state variable distribution,
#' and the second dimension corresponds to time
#' steps.}
#' \item{\strong{proto_ipm}}{: the \code{proto_ipm} object used to implement
#' the model.}
#' }
#'
#' In addition to the list class, each object will have a class comprised of the
#' arguments from \code{init_ipm} plus \code{'ipm'} pasted together with
#' underscores. This is to facilitate \code{print}, \code{plot}, and
#' \code{lambda} methods. For example, a \code{init_ipm("general", "di", "det")}
#' model will have the class \code{'general_di_det_ipm'} once it has been
#' implemented using \code{make_ipm}.
#'
#' @export
make_ipm <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...) {
UseMethod('make_ipm')
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = TRUE,
iteration_direction = "right"
) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function definitions
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_simple(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
if(iterate) {
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kern_list,
iterations,
temp$pop_state,
main_env,
k_row,
normalize_pop_size)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- all_sub_kerns$env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
out_seq <- NA_integer_
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(sub_kernels = sub_kern_list,
env_list = out_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_det_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right") {
if(iterate && all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# checks pop_state, env_state, domain_definitions
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)){
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
# construct the kernels from their function defintions
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_simple(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
if(iterate) {
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kern_list,
iterations,
kern_seq,
temp$pop_state,
main_env,
k_row,
normalize_pop_size,
report_progress)
# In order to operate properly, we had to insert an extra entry in
# lambda to make sure it had the same length as pop_state (it'll always be
# one fewer in reality though). The first entry is ALWAYS NA, but that doesn't
# seem user friendly (ipmr-internal friend either, tbh). After that,
# we need to conver the names of the trait distributions back to the
# user-supplied ones
pop_state$lambda <- pop_state$lambda[-1]
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(sub_kernels = sub_kern_list,
env_list = out_ret,
env_seq = kern_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_stoch_kern_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_di_stoch_param <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# initialize the pop_state vectors in main_env so they can be found
# at evaluation time
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# list to hold the possibly returned evaluation environments
env_list <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = 'n')
sub_kernels <- sys$ipm_system$sub_kernels
# .iterate_model is internal generic - see internal-model_iteration.R
pop_state <- .iterate_model(proto_ipm,
sub_kernels,
current_iteration = i,
kern_seq = kern_seq,
temp$pop_state,
main_env,
k_row,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(!return_main_env) {
env_list <- NA_character_
}
temp <- .update_param_output(
sub_kernels,
pop_state,
env_list,
main_env,
temp,
iterations,
i,
return_sub_kernels
)
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
temp$pop_state$lambda <- temp$pop_state$lambda[-1]
names(temp$pop_state) <- gsub("^pop_state_", "n_", names(temp$pop_state))
out <- list(
sub_kernels = temp$sub_kernels,
env_list = env_list,
env_seq = temp$env_seq,
pop_state = temp$pop_state,
proto_ipm = proto_ipm
)
out$sub_kernels <- set_ipmr_classes(out$sub_kernels)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_di_stoch_param_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = TRUE,
iteration_direction = "right") {
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# initialize others + k_row
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain,
usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# Thus far, I think general_* methods will have to use iteration for lambdas
# as I'm not sure I want to work out the correct cbind(rbind(...)) rules for
# creating a mega-matrix. So throw an error if pop_state isn't defined
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_general(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
# Things switch up here from the simple_* versions. Rather than construct a mega-K
# through rbind + cbinding, we just iterate the population vector with the
# sub kernels. This means I don't have to overload all of the arithmetic
# operators and keeps things simpler internally. It also means there's no
# need to implement sparse kernels/matrices for things like an age x size IPM.
if(iterate) {
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kern_list,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
}
# Final bits of housekeeping post-iteration. .prep_other_output deals specifically
# with items that depend on `return_all_envs` and `iterate` switches, but have
# length > 1 (ifelse() output always equals length(input))
sub_kern_list <- set_ipmr_classes(sub_kern_list)
temp_other_out <- .prep_other_output(env_list,
kern_seq = NA_integer_,
pop_state,
return_main_env,
return_all_envs,
iterate)
env_ret <- temp_other_out$env_ret
env_seq_ret <- temp_other_out$env_seq_ret
pop_ret <- temp_other_out$pop_ret
out <- list(
sub_kernels = sub_kern_list,
env_list = env_ret,
env_seq = env_seq_ret,
pop_state = pop_ret,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_det_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right") {
if(iterate && all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
# initialize others + k_row
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# Thus far, I think general_* methods will have to use iteration for lambdas
# as I'm not sure I want to work out the correct cbind(rbind(...)) rules for
# creating a mega-matrix. So throw an error if pop_state isn't defined
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
env_list <- list(main_env = main_env)
all_sub_kerns <- .make_sub_kernel_general(others,
env_list,
return_envs = return_all_envs)
sub_kern_list <- all_sub_kerns$sub_kernels
env_list <- all_sub_kerns$env_list
if(iterate) {
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kern_list,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
pop_state$lambda <- pop_state$lambda[-1]
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
}
# Final bits of housekeeping post-iteration. .prep_other_output deals specifically
# with items that depend on `return_all_envs` and `iterate` switches, but have
# length > 1 (ifelse() output always equals length(input))
temp_other_out <- .prep_other_output(env_list,
kern_seq,
pop_state,
return_main_env,
return_all_envs,
iterate)
env_ret <- temp_other_out$env_ret
env_seq_ret <- temp_other_out$env_seq_ret
pop_ret <- temp_other_out$pop_ret
sub_kern_list <- set_ipmr_classes(sub_kern_list)
out <- list(
sub_kernels = sub_kern_list,
env_list = env_ret,
env_seq = env_seq_ret,
pop_state = pop_ret,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_stoch_kern_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_di_stoch_param <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NULL,
normalize_pop_size = TRUE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Work out whether to append usr_funs to proto or to restore them from prior
# implemenation. Logic is documented in make_ipm.simple_di_det()
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
}
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repetition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row, proto_ipm, iterations,
normalize_pop_size)
# initialize the pop_state vectors in main_env so they can be found
# at evaluation time
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
if(!iterate || iterations < 1) {
stop("All 'general_*_stoch_param' models must be iterated at least once!",
call. = FALSE)
}
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
env_ret <- list()
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_general_lazy(others,
main_env,
return_envs = return_all_envs)
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration.
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
kern_seq = kern_seq,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$ipm_system$env_list$main_env <- NULL
names(sys$ipm_system$env_list) <- paste(names(sys$ipm_system$env_list),
"it", i, sep = "_")
env_ret <- c(env_ret, sys$ipm_system$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
temp$pop_state$lambda <- temp$pop_state$lambda[-1]
names(temp$pop_state) <- gsub("^pop_state_", "n_", names(temp$pop_state))
out <- list(
sub_kernels = temp$sub_kernels,
env_list = c(list(main_env = main_env), temp$sub_kernel_envs),
env_seq = temp$env_seq,
pop_state = temp$pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
out$sub_kernels <- set_ipmr_classes(out$sub_kernels)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_di_stoch_param_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
# Density dependent methods----------
#' @rdname make_ipm
#' @export
make_ipm.simple_dd_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# 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)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- NA_integer_
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_det_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_dd_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
temp$sub_kernels <- list()
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- kern_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_stoch_kern_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.simple_dd_stoch_param <-function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "n")
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
names(sub_kernels) <- paste(names(sub_kernels),
"it",
i,
sep = "_")
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
temp$env_seq <- data.frame(temp$env_seq, stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
out_seq <- temp$env_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
class(out) <- c('simple_dd_stoch_param_ipm',
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_det <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE
) {
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
main_env <- .bind_all_constants(env_state = others$env_state[[1]],
env_to_bind = main_env)
temp <- .prep_di_output(others, k_row,
proto_ipm, iterations,
normalize_pop_size)
if(all(is.na(proto_ipm$pop_state[[1]]))) {
stop("All general_* IPMs must have a 'pop_state' defined.",
"See ?define_pop_state() for more details." )
} else {
main_env <- .add_pop_state_to_main_env(temp$pop_state, main_env)
}
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
for(i in seq_len(iterations)) {
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
temp$pop_state,
main_env,
normalize_pop_size)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# 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)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
if(return_all_envs) {
out_ret <- temp$env_list
} else if(return_main_env) {
out_ret <- list(main_env = main_env)
} else {
out_ret <- NA_character_
}
out_seq <- NA_integer_
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_det_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_stoch_kern <- function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq))) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "y")
sub_kernels <- sys$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
out_seq <- kern_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_stoch_kern_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
#' @rdname make_ipm
#'
#' @export
make_ipm.general_dd_stoch_param <-function(proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...,
domain_list = NULL,
iterate = TRUE,
iterations = 50,
kernel_seq = NA_character_,
normalize_pop_size = FALSE,
report_progress = FALSE,
iteration_direction = "right",
return_sub_kernels = FALSE) {
if(iterate &&
all(is.null(kernel_seq) | is.na(kernel_seq)) &&
any(proto_ipm$uses_par_sets)) {
message("'kernel_seq' not defined. Will generate one internally")
kernel_seq <- "internal"
}
# Figure out if we're dealing with a new model or an old one that is
# being reimplemented. If the proto is not new and usr_funs isn't passed to the new
# make_ipm call, then restore the old version. If usr_funs are passed,
# we append them regardless of whether or not the model has been implemented.
if(isTRUE(attr(proto_ipm, 'implemented')) && rlang::is_empty(usr_funs)) {
if(!is.na(proto_ipm$usr_funs[[1]][1])) {
usr_funs <- proto_ipm$usr_funs[[1]]
}
} else if(!rlang::is_empty(usr_funs)) {
proto_ipm <- .append_usr_funs_to_proto(proto_ipm, usr_funs)
}
proto_list <- .initialize_kernels(proto_ipm, iterate, iteration_direction)
others <- proto_list$others
k_row <- proto_list$k_row # k_row is either an NA or a proto_ipm object
# Initialize the main_environment so these values can all be found at
# evaluation time
if(is.null(domain_list)) {
main_env <- .make_main_env(others$domain, usr_funs,
age_size = .uses_age(others))
} else {
main_env <- .make_main_env(domain_list, usr_funs,
age_size = .uses_age(others))
proto_ipm$domain <- I(list(domain_list))
}
temp <- .prep_di_output(others,
k_row,
proto_ipm,
iterations,
normalize_pop_size)
# Bind env_exprs, constants, and pop_vectors to main_env so that
# we can always find them and avoid that miserable repitition
main_env <- .bind_all_constants(env_state = others$env_state[[1]]$constants,
env_to_bind = main_env)
main_env <- .add_pop_state_to_main_env(temp$pop_state,
main_env)
# construct the kernels from their function defintions
# env_list <- list(main_env = main_env)
others <- .prep_dd_vr_exprs(others)
k_row <- .prep_dd_k_exprs(k_row)
if(iterate) {
env_ret <- list(main_env = main_env)
kern_seq <- .make_kern_seq(others,
iterations,
kernel_seq)
for(i in seq_len(iterations)) {
# add the variable to let the user access the current iteration.
.bind_iter_var(main_env, i)
.stoch_progress_message(report_progress, iterations, i)
# Lazy variant makes sure that whatever functions that generate parameter
# values stochastically are only evaluated 1 time per iteration! This is so
# multiple parameters meant to come from a joint distribution really come from
# the joint distribution!
sys <- .make_sub_kernel_simple_lazy(others,
main_env,
return_envs = return_all_envs,
dd = "n")
sub_kernels <- sys$ipm_system$sub_kernels
# Generate the pop_state for a single iteration! This is critical to ensuring
# env_state_funs are only evaluated once per iteration. kern_seq = NULL
# because the environmental parameters are generated on the fly by the
# user defined function
pop_state <- .iterate_model(proto_ipm,
k_row,
sub_kernels,
current_iteration = i,
iterations,
kern_seq,
temp$pop_state,
main_env,
normalize_pop_size,
report_progress)
if(return_all_envs) {
sys$env_list$main_env <- NULL
names(sys$env_list) <- paste(names(sys$env_list), "it", i, sep = "_")
env_ret <- c(env_ret, sys$env_list)
} else if(return_main_env) {
env_ret <- list(main_env = main_env)
} else{
env_ret <- NA_character_
}
temp <- .update_param_output(sub_kernels,
pop_state,
env_ret,
main_env,
temp,
iterations,
i,
return_sub_kernels)
}
# Remove the NA at 1 - it is a dummy so that purrr::map2 can work
# properly
pop_state[grepl('lambda', names(pop_state))] <- lapply(
pop_state[grepl("lambda", names(pop_state))],
function(x) {
out <- x[ , -1, drop = FALSE]
return(out)
}
)
# Convert pop_state names back to the user-supplied ones
names(pop_state) <- gsub("^pop_state_", "n_", names(pop_state))
} else {
pop_state <- NA_real_
}
temp$env_seq <- data.frame(temp$env_seq,
stringsAsFactors = FALSE)
if(!is.null(kern_seq)) {
temp$env_seq <- cbind(temp$env_seq,
kernel_seq = kern_seq)
}
out_seq <- temp$env_seq
temp$sub_kernels <- set_ipmr_classes(temp$sub_kernels)
out <- list(sub_kernels = temp$sub_kernels,
env_list = env_ret,
env_seq = out_seq,
pop_state = pop_state,
proto_ipm = proto_ipm
)
attr(out$proto_ipm, 'implemented') <- TRUE
attr(out, "iterated") <- (iterate && iterations >= 1)
if(inherits(proto_ipm, "age_x_size")) {
a_s_class <- "age_x_size_ipm"
} else {
a_s_class <- NULL
}
class(out) <- c('general_dd_stoch_param_ipm',
a_s_class,
'ipmr_ipm',
'list')
return(out)
}
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