ipmr: lambda – R documentation

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lambda

Compute the per-capita growth rate for an IPM object

Description

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.

Usage

lambda(ipm, ...)

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

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

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

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

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

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

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

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

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

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

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

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

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 is the mean of the log'd per-capita growth rate from each time step.
`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 hierarchical effects (if any). For other types, a numeric vector.

ipmr

Fits Integral Projection Models Using an Expression Based Framework

v0.0.1

MIT + file LICENSE

Authors

Sam Levin [aut, cre] (<https://orcid.org/0000-0002-3289-9925>), Aldo Compagnoni [aut], Dylan Childs [aut], Sanne Evers [aut], Roberto Salguero-Gomez [aut], Tiffany Knight [aut]

Initial release