print_star: Print proto_ipms or *_ipm objects
In levisc8/ipmr: Integral Projection Models

print.proto_ipm

R Documentation

Print proto_ipms or *_ipm objects

Description

Print proto_ipms or *_ipm objects

Generics for IPM classes

Usage

## 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.