Compute the per-capita growth rate for an IPM object

Source: R/generics.R

Compute the per-capita growth rate for a given model. Can handle stochastic and deterministic models, and has the option to discard burn in for stochastic models.

lambda(ipm, ...)

# S3 method for class 'simple_di_det_ipm'
lambda(ipm, type_lambda = "last", log = FALSE, ...)

# S3 method for class 'simple_di_stoch_kern_ipm'
lambda(ipm, type_lambda = "stochastic", burn_in = 0.1, log = NULL, ...)

# S3 method for class 'simple_di_stoch_param_ipm'
lambda(ipm, type_lambda = "stochastic", burn_in = 0.1, log = NULL, ...)

# S3 method for class 'general_di_det_ipm'
lambda(ipm, type_lambda = "last", log = FALSE, ...)

# S3 method for class 'general_di_stoch_kern_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

# S3 method for class 'general_di_stoch_param_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

# S3 method for class 'simple_dd_det_ipm'
lambda(ipm, type_lambda = "all", ..., log = FALSE)

# S3 method for class 'simple_dd_stoch_kern_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

# S3 method for class 'simple_dd_stoch_param_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

# S3 method for class 'general_dd_det_ipm'
lambda(ipm, type_lambda = "last", ..., log = FALSE)

# S3 method for class 'general_dd_stoch_kern_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

# S3 method for class 'general_dd_stoch_param_ipm'
lambda(ipm, ..., type_lambda = "stochastic", burn_in = 0.1, log = NULL)

Arguments

ipm

An object returned by make_ipm().

...

other arguments passed to methods.

type_lambda

Either 'all', 'last', or 'stochastic'. 'all' returns a vector of lambda values for each time step of the simulation (equal in length to the iterations argument of make_ipm()). 'last' returns the lambda value for the final timestep. 'stochastic' returns a single value, which by default is mean(log(lambda(ipm, type_lambda = "all"))), with the proportion of burn_in iterations removed from the beginning of the simulation. Set log to FALSE to get lambda on the linear scale for stochastic models (i.e. exp(mean(log(lambdas)))).

log

Return lambda on the log scale? This is TRUE by default for stochastic models, and FALSE for deterministic models.

burn_in

The proportion of iterations to discard. Default is 0.1 (i.e. first 10% of iterations in the simulation).

Value

When type_lambda = "all", an array. Rows correspond to time steps, and columns correspond to parameter sets (if any). For other types, a numeric vector.