The make_ipm.* methods convert a proto_ipm into a
set of discretized kernels and population vectors. Methods have different
requirements, so be sure to read the parameter documentation.
vignette('ipmr-introduction', 'ipmr') a more complete introduction.
make_ipm(
proto_ipm,
return_main_env = TRUE,
return_all_envs = FALSE,
usr_funs = list(),
...
)
# S3 method for class 'simple_di_det'
make_ipm(
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"
)
# S3 method for class 'simple_di_stoch_kern'
make_ipm(
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"
)
# S3 method for class 'simple_di_stoch_param'
make_ipm(
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
)
# S3 method for class 'general_di_det'
make_ipm(
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"
)
# S3 method for class 'general_di_stoch_kern'
make_ipm(
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"
)
# S3 method for class 'general_di_stoch_param'
make_ipm(
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
)
# S3 method for class 'simple_dd_det'
make_ipm(
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
)
# S3 method for class 'simple_dd_stoch_kern'
make_ipm(
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
)
# S3 method for class 'simple_dd_stoch_param'
make_ipm(
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
)
# S3 method for class 'general_dd_det'
make_ipm(
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
)
# S3 method for class 'general_dd_stoch_kern'
make_ipm(
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
)
# S3 method for class 'general_dd_stoch_param'
make_ipm(
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
)A proto_ipm. This should be the
output of define_kernel, or the define_* functions.
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
TRUE.
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 *_stoch_param model).
Default is FALSE.
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 ... or formula.
Other arguments passed to methods.
An optional list of new domain information to implement the IPM with.
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.
If iterate is TRUE, then the number of iterations
to run the model for.
A logical indicating whether to re-scale the population
vector to sum to 1 before each iteration. Default is TRUE for
*_di_* methods and FALSE for *_dd_* methods.
Either "right" (default) or "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.
For *_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 iterations.
This should be a vector containing values of the parameter set indices specified
in par_set_indices, or empty. Support for Markov chains will eventually
get implemented. If it is empty, make_ipm will try to generate a
sequence internally using a random selection of the par_set_indices
defined in define_kernel.
A logical indicating whether or not to periodically
report progress for a stochastic simulation. Does not apply to deterministic
methods. Default is FALSE.
Only applies to density dependent and parameter
resampled models. If 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 FALSE.
The make_ipm.* methods will always return a list of length 5
containing the following components:
sub_kernels: a list of arrays specified in define_kernel.
env_list: a list containing the evaluation environments of
kernel. This will contain the main_env object
if return_main_env = TRUE. It will also contain
the sub-kernels evaluation environments if
return_all_envs = TRUE.
env_seq: a character vector with length 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 n_iterations rows.
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.
proto_ipm: the 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 init_ipm plus 'ipm' pasted together with
underscores. This is to facilitate print, plot, and
lambda methods. For example, a init_ipm("general", "di", "det")
model will have the class 'general_di_det_ipm' once it has been
implemented using make_ipm.