frair (version 0.5.203)

fr_hassIIInr: Hassell's Type III Response, without replacement

Description

Hassell's type-III response (not assuming replacement)

Usage

hassIIInr_fit(data, samp, start, fixed, boot=FALSE, windows=FALSE)
    hassIIInr_nll(b, c, h, T, X, Y)
    hassIIInr(X, b, c, h, T)

Arguments

data

A data frame containing X and Y (at least).

samp

A vector specifying the rows of data to use in the fit. Provided by boot() or manually, as required.

start

A named list. Starting values for items to be optimised. Usually b, c and h.

fixed

A names list. 'Fixed data' (not optimised). Usually T.

boot

A logical. Is the function being called for use by boot()?

windows

A logical. Is the operating system Microsoft Windows?

b,c,h

Hassel's b and c, plus h, the handling time. Usually items to be optimised.

T

T, the total time available.

X

The X variable. Usually prey density.

Y

The Y variable. Usually the number of prey consumed.

Author

Daniel Pritchard

Details

This implements Hassel's Type-III extension to the 'random predator' functional response. This does not assume prey are replaced throughout the experiment (c.f. hassIII). The number of prey eaten (Ne) follow the same relationship defined for the Roger's Type-II response, however the capture rate (a) is assumed to vary with prey density in the following hyperbolic relationship:

a <- (b*X)/(1+c*X)

where b and c are coefficients to be fitted and X is the initial prey density. This is the initial formulation of Hassell et al. (1977) and uses their naming conventions. The value for a is then used within the Roger's Type-II 'random predator' equation (see rogersII).

None of these functions are designed to be called directly, though they are all exported so that the user can do so if desired. The intention is that they are called via frair_fit, which calls them in the order they are specified above.

hassIIInr_fit does the heavy lifting and also pulls double duty as the statistic function for bootstrapping (via boot() in the boot package). The windows argument if required to prevent needless calls to require(frair) on platforms that can manage sane parallel processing.

The core fitting is done by mle2 from the bbmle package and users are directed there for more information. mle2 uses the hassIIInr_nll function to optimise hassIIInr.

Further references and recommended reading can be found on the help page for frair_fit.

References

Hassell M, Lawton J, Beddington J (1977) Sigmoid functional responses by invertebrate predators and parasitoids. Journal of Animal Ecology 46: 249--262.

See Also

frair_fit.

Examples

datx <- rep(c(1,2,3,4,6,12,24,50,100), times=10)
daty1 <- round(hassIIInr(X=datx, 
            b=0.08*rnorm(length(datx), mean=1, sd=0.1), 
            c=0.1*rnorm(length(datx), mean=1, sd=0.1), 
            h=0.08*rnorm(length(datx), mean=1, sd=0.1), 
            T=1),0)
daty2 <- round(hassIIInr(X=datx, 
            b=0.05*rnorm(length(datx), mean=1, sd=0.1), 
            c=0.08*rnorm(length(datx), mean=1, sd=0.1), 
            h=0.1*rnorm(length(datx), mean=1, sd=0.1), 
            T=1),0)
dat <- data.frame(datx,daty1,daty2)
RcppParallel::setThreadOptions(numThreads = 2)
hassIIInr_1 <- frair_fit(daty1~datx, data=dat, response='hassIIInr', 
        start=list(b=0.05, c=0.1, h=0.1), fixed=list(T=1))
hassIIInr_2 <- frair_fit(daty2~datx, data=dat, response='hassIIInr', 
        start=list(b=0.05, c=0.1, h=0.1), fixed=list(T=1))

plot(c(0,100), c(0,15), type='n', xlab='Density', ylab='No. Eaten')
points(hassIIInr_1)
points(hassIIInr_2, col=4)
lines(hassIIInr_1)
lines(hassIIInr_2, col=4)

frair_compare(hassIIInr_1, hassIIInr_2)