Padrino methods for `ipmr` generic functions

Provides wrappers around ipmr generic functions to extract some quantities of interest from pdb_proto_ipm_lists and pdb_ipms.

# S3 method for pdb_proto_ipm_list
vital_rate_exprs(object)

# S3 method for pdb_ipm
vital_rate_exprs(object)

# S3 method for pdb_proto_ipm_list
kernel_formulae(object)

# S3 method for pdb_ipm
kernel_formulae(object)

# S3 method for pdb_proto_ipm_list
domains(object)

# S3 method for pdb_ipm
domains(object)

# S3 method for pdb_proto_ipm_list
parameters(object)

# S3 method for pdb_ipm
parameters(object)

# S3 method for pdb_proto_ipm_list
pop_state(object)

# S3 method for pdb_ipm
pop_state(object)

# S3 method for pdb_ipm
vital_rate_funs(ipm)

# S3 method for pdb_ipm
int_mesh(ipm, full_mesh = TRUE)

# S3 method for pdb_ipm
lambda(ipm, ...)

# S3 method for pdb_ipm
right_ev(ipm, iterations = 100, tolerance = 1e-10, ...)

# S3 method for pdb_ipm
left_ev(ipm, iterations = 100, tolerance = 1e-10, ...)

# S3 method for pdb_ipm
is_conv_to_asymptotic(ipm, tolerance = 1e-10, burn_in = 0.1)

# S3 method for pdb_ipm
conv_plot(ipm, iterations = NULL, log = FALSE, show_stable = TRUE, ...)

# S3 method for pdb_ipm
make_iter_kernel(ipm, ..., name_ps = NULL, f_forms = NULL)

# S3 method for pdb_ipm
mean_kernel(ipm)

pdb_new_fun_form(...)

# S3 method for pdb_proto_ipm_list
parameters(object, ...) <- value

# S3 method for pdb_proto_ipm_list
vital_rate_exprs(object, kernel = NULL, vital_rate = NULL) <- value

# S3 method for pdb_proto_ipm_list
kernel_formulae(object, kernel) <- value

# S3 method for pdb_ipm
[(x, i)

Arguments

object: An object produced by pdb_make_proto_ipm or pdb_make_ipm.
ipm: A pdb_ipm.
full_mesh: Logical. Return the complete set of meshpoints or only the unique ones.
...: Usage depends on the function - see Details and Examples.
iterations: The number of times to iterate the model to reach convergence. Default is 100.
tolerance: Tolerance to evaluate convergence to asymptotic dynamics.
burn_in: The proportion of iterations to discard as burn in when assessing convergence.
log: Log-transform lambdas for plotting?
show_stable: Show horizontal line denoting stable population growth?
name_ps: For pdb_ipm objects that contain age_x_size IPMs, a named list. The names of the list should be the ipm_ids that are age_x_size models, and the values in the list should be the the name of the survival/growth kernels.
f_forms: For pdb_ipm objects that contain age_x_size IPMs, a named list. The names of the list should be the ipm_ids that are age_x_size models, and the values in the list should be the the name of the fecundity kernels. If multiple sub-kernels contribute to fecundity, we can also supply a string specifying how they are combined (e.g. f_forms = "F + C").
value: The value to insert. See details and Examples.
kernel: Ignored, present for compatibility with ipmr.
vital_rate: Ignored, present for compatibility with ipmr.
x: A pdb_ipm object.
i: The index to extract

Value

Most of these return named lists where names correspond to ipm_ids. The exception is pdb_new_fun_form, which returns a list of expressions. It is only intended for setting new expressions with vital_rate_exprs<-.

Details

There are number of uses for ... which depend on the function used for them. These are described below.

`pdb_new_fun_form`

This must be used when setting new expressions for vital rates and kernel formulae. The ... argument should be a named list of named lists. The top most layer should be ipm_id's. The next layer should be a list where the names are vital rates you wish to modify, and the values are the expressions you want to insert. See examples.

`make_iter_kernel`

The ... here should be expressions representing the block kernel of the IPMs in question. The names of each expression should be the ipm_id, and the expressions should take the form of c(<upper_left>, <upper_right>, <lower_left>, <lower_right>) (i.e. a vector of symbols would create a matrix in row-major order). See examples.

`conv_plot`/`lambda`

The ... are used pass additional arguments to lambda and conv_plot.

Examples


data(pdb)
my_pdb <- pdb_make_proto_ipm(pdb, c("aaaa17", "aaa310"))
#> 'ipm_id' aaa310 has the following notes that require your attention:
#> aaa310: 'Geo and time info retrieved from COMPADRE (v.X.X.X.4)'

# These values will be appended to the parameter list for each IPM, as they
# aren't currently present in them.

parameters(my_pdb) <- list(
  aaa310 = list(
    g_slope_2 = 0.0001,
    establishment_prob = 0.02
  ),
  aaaa17 = list(
    g_var = 4.2,
    germ_prob = 0.3
  )
)

# We can overwrite a parameter value with a new one as well. Old values aren't
# saved anywhere except in the pdb object, so be careful!

parameters(my_pdb) <- list(
  aaa310 = list(
    s_s    = 0.93, # old value is 0.92
    gvar_i = 0.13 # old value is 0.127
  )
)

vital_rate_exprs(my_pdb) <- pdb_new_fun_form(
    list(
      aaa310 = list(mu_g = g_int + g_slope * size_1 + g_slope_2 * size_1^2),
      aaaa17 = list(sigmax2 = sqrt(g_var * exp(cfv1 + cfv2 * size_1))
     )
   )
 )

 kernel_formulae(my_pdb) <- pdb_new_fun_form(
   list(
     aaaa17 = list(Y = recr_size * yearling_s * germ_prob * d_size),
     aaa310 = list(F = f_n * f_d * establishment_prob)
   )
 )

 my_ipms    <- pdb_make_ipm(my_pdb)
 iter_kerns <- make_iter_kernel(my_ipms, aaaa17 = c(0, F_yr, Y, P_yr))