gause_wrapper: Automated wrapper for Gause fitting functions
In gauseR: Lotka-Volterra Models for Gause's 'Struggle for Existence'

gause_wrapper

R Documentation

Automated wrapper for Gause fitting functions

Description

Automatically runs routine for finding starting values and optimal parameter values for a Lotka-Volterra interaction system. Using the default functions, species dynamics follow the form dni/dt = ni * (ri + aii * ni + sum_j(aij * nj)) where ri are the elements of vector r, and aij are the elements of matrix A.

Usage

gause_wrapper(
  time,
  species,
  N_starting = NULL,
  r_starting = NULL,
  A_starting = NULL,
  doplot = TRUE,
  keeptimes = FALSE,
  parm_signs = NULL,
  doopt = TRUE,
  ...
)

Arguments

`time`	Vector of time steps corresponding to observations in species data.frame.
`species`	A data.frame with one column per species to be fitted. Note - column names cannot include white spaces or non-standard special characters.
`N_starting`	Optional starting values for initial abundances.
`r_starting`	Optional starting values for species growth rates. If a value is set to zero, it #forces that parameter to zero in the fitting. Values of NA are ignored. Defaults to NULL (no starting values).
`A_starting`	Optional starting values for species interaction coefficients. If a value is set to zero, it #forces that parameter to zero in the fitting. Values of NA are ignored. Defaults to NULL (no starting values).
`doplot`	Logical. Should the resulting model be plotted? Defaults to TRUE.
`keeptimes`	Should predictions be given for the points in the "time" vector, or for a list of 100 evenly spaced time points? Defaults to FALSE.
`parm_signs`	Optional variable specifying signs for parameters. Defaults to NULL (automatically selected).
`doopt`	Should optimizer be used (if TRUE), or should the initial linearized estimates by applied (if FALSE)? Defaults to TRUE.
`...`	Optional additional arguments to be passed to ode and optim functions.

Value

A list with simulated time series (out), paramter estimates (parameter_intervals), optimizer output (optout), and raw data used for fitting (rawdata).

Examples


#load competition data
data("gause_1934_science_f02_03")

#subset out data from species grown in mixture
mixturedat<-gause_1934_science_f02_03[gause_1934_science_f02_03$Treatment=="Mixture",]

#extract time and species data
time<-mixturedat$Day
species<-data.frame(mixturedat$Volume_Species1, mixturedat$Volume_Species2)
colnames(species)<-c("P_caudatum", "P_aurelia")

#run wrapper
gause_out<-gause_wrapper(time=time, species=species)

gauseR documentation built on Oct. 23, 2023, 1:08 a.m.