QFASA (version 1.2.1)

comp.rep: Repeatability in Diet Estimates

Description

Computes a measure of repeatability for a sample of predators with repeated diet estimate measurements.

Usage

comp.rep(
  data,
  prey.database,
  fatcont.mat,
  dist.meas,
  ext.fa,
  B = 50,
  R = 100,
  CI = FALSE,
  alpha = 0.05,
  gamma.QFASA = 1,
  gamma.rho = 1
)

Value

Bias corrected measure of repeatability, estimate of the bias and (if CI=TRUE) a confidence interval for the true repeatability.

Arguments

data

data frame of diet estimates. First column must denote the predator and second column the second factor (eg. year or season).

prey.database

prey data base that was used to compute the QFASA diet estimates in data. Will be used to generate pseudo predators.

fatcont.mat

data frame or matrix of length equal to the number of prey FA signatures in prey data base. First column is name of species and second column is lipid.

dist.meas

distance measure to use in p.QFASA.

ext.fa

subset of FAs to use.

B

number of pseudo predators samples to generate for bias calculation. Default is set to 50 because is slow to run.

R

number of bootstrap samples (i.e. R samples for each generated sample of pseudo predators). Default is set to 100 because it is slow to run.

CI

indicates if a confidence interval for rho is to be calculated. Default is FALSE since this is time consuming to obtain.

alpha

a (1-alpha/2)X100 percent confidence interval is calculated for rho if CI=TRUE.

gamma.QFASA

if dist.meas=3, gamma is required. Default is 1.

gamma.rho

value of gamma to be used to compute CS distance in repeatablity functions. Default is 1.

References

"Repeatability for Compositional Diet Estimates with Zeros". Contact Connie Stewart ([e-mail]).

Examples

 ##  These examples take some time to run.
 ##  Please uncomment code below to run them.

# data(preyFAs)
# data(FAset)

## Balanced Diet Data

#my.preybase <- preyFAs[,-c(1,3)]
#my.preybase[,-1] <- my.preybase[,-1]/rowSums(my.preybase[,-1])

#set.seed(10)

#comp.rep(data = bal.diet.data,prey.database=my.preybase,
#fatcont.mat = as.data.frame(preyFAs[,c(2,3)]),dist.meas=2,
#ext.fa = as.vector(unlist(FAset)))

## Unbalanced Diet Data

# my.preybase <- preyFAs[,-c(1,3)]
# my.preybase[,-1] <- my.preybase[,-1]/rowSums(my.preybase[,-1])

# set.seed(10)

# comp.rep(unbal.diet.data,my.preybase,as.data.frame(preyFAs[,c(2,3)]),2,
# as.vector(unlist(FAset)))