gause_wrapper {gauseR}

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)