Print proto_ipms or *_ipm objects

Print proto_ipms or *_ipm objects

Generics for IPM classes

# S3 method for class 'proto_ipm'
print(x, ...)

# S3 method for class 'simple_di_det_ipm'
print(
  x,
  comp_lambda = TRUE,
  type_lambda = "last",
  sig_digits = 3,
  check_conv = TRUE,
  ...
)

# S3 method for class 'simple_dd_det_ipm'
print(x, comp_lambda = TRUE, type_lambda = "last", sig_digits = 3, ...)

# S3 method for class 'simple_di_stoch_kern_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'simple_dd_stoch_kern_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'simple_di_stoch_param_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'simple_dd_stoch_param_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'general_di_det_ipm'
print(
  x,
  comp_lambda = TRUE,
  type_lambda = "last",
  sig_digits = 3,
  check_conv = TRUE,
  ...
)

# S3 method for class 'general_dd_det_ipm'
print(x, comp_lambda = TRUE, type_lambda = "last", sig_digits = 3, ...)

# S3 method for class 'general_di_stoch_kern_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'general_dd_stoch_kern_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'general_di_stoch_param_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

# S3 method for class 'general_dd_stoch_param_ipm'
print(x, comp_lambda = TRUE, type_lambda = "stochastic", sig_digits = 3, ...)

Arguments

x

An object of class proto_ipm or produced by make_ipm().

...

Ignored

comp_lambda

A logical indicating whether or not to calculate lambdas for the iteration kernels and display them.

type_lambda

Either 'all' or 'stochastic'. See lambda for more details.

sig_digits

The number of significant digits to round to if comp_lambda = TRUE.

check_conv

A logical: for deterministic models, check if population state has converged to asymptotic dynamics? If TRUE and the model has not converged, a message will be printed.

Value

x invisibly.

Details

For printing proto_ipm objects, indices are wrapped in <index> to assist with debugging. These are not carried into the model, just a visual aid.