parameter estimation

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Description

Estimates parameters of predator preferences model and calculates LRT. Eaten and caught dataframes are indexed with rows across time points and columns of prey species.

Usage

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predPref(eaten, caught, hypotheses = c("c", "Ct"), alpha = 0.05,
  em_maxiter = 1000)

Arguments

eaten

a dataframes of eatings preferences; TxS

caught

a dataframes of caught prey species; TxS

hypotheses

a 2-tuple specifying the null and alternative hypotheses, respectively

alpha

LRT level of significance

em_maxiter

maximum number of iterations allowed for EM algorithm

Value

A list of class 'predPref' with the following elements:

null: parameters as estimated under the specified null hypothesis.

alt: parameters as estimated under the specified alternative hypothesis.

loglikH0: the null hypothesis log-likelihood, with constants not accounted for.

loglikH1: the alternative hypothesis log-likelihood, with constants not accounted for.

J: a column vector of dimension T containing the number of predators in each time period.

I: a column vector of dimension T containing the number of traps in each time period.

LRT: the likelihood ratio test statistics.

hypotheses: a 2-tuple of the user specified hypotheses.

data.name: a character string giving the names of the data.

See Also

simPref summary.predPref

Examples

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# set parameters
Predators <- Traps <- 100
PreySpecies <- 2
Times <- 5
g <- matrix(sqrt(2), nrow=Times, ncol=PreySpecies)     # gamma
l <- matrix(seq(0.4,1.8,length.out=5)*sqrt(2), nrow=Times, ncol=PreySpecies) # ct

# fit model
## Not run: 
fdata <- simPref(PreySpecies, Times, Predators, Traps, l, g, EM=FALSE)
predPref(fdata$eaten, fdata$caught, hypotheses=c('ct', 'cst'))

## End(Not run)