Nothing
## Hassell's Type III pred-prey function not assuming replacement.
# hassIIInr: The guiding function...
hassIIInr <- function(X, b, c, h, T) {
if(is.list(b)){
coefs <- b
b <- coefs[['b']]
c <- coefs[['c']]
h <- coefs[['h']]
T <- coefs[['T']]
}
a <- (b*X)/(1+c*X) # From Hassell et al (1977)
return(X - lamW::lambertW0(a * h * X * exp(-a * (T - h * X)))/(a * h)) # Direct from rogersII
}
# hassIIInr_fit: Does the heavy lifting
hassIIInr_fit <- function(data, samp, start, fixed, boot=FALSE, windows=FALSE) {
# Setup windows parallel processing
fr_setpara(boot, windows)
samp <- sort(samp)
dat <- data[samp,]
out <- fr_setupout(start, fixed, samp)
try_hassIIInr <- try(bbmle::mle2(hassIIInr_nll, start=start, fixed=fixed, data=list('X'=dat$X, 'Y'=dat$Y),
optimizer='optim', method='Nelder-Mead', control=list(maxit=5000)),
silent=T)
if (inherits(try_hassIIInr, "try-error")) {
# The fit failed...
if(boot){
return(out)
} else {
stop(try_hassIIInr[1])
}
} else {
# The fit 'worked'
for (i in 1:length(names(start))){
# Get coefs for fixed variables
cname <- names(start)[i]
vname <- paste(names(start)[i], 'var', sep='')
out[cname] <- coef(try_hassIIInr)[cname]
out[vname] <- vcov(try_hassIIInr)[cname, cname]
}
for (i in 1:length(names(fixed))){
# Add fixed variables to the output
cname <- names(fixed)[i]
out[cname] <- as.numeric(fixed[cname])
}
if(boot){
return(out)
} else {
return(list(out=out, fit=try_hassIIInr))
}
}
}
# hassIIInr_nll
hassIIInr_nll <- function(b, c, h, T, X, Y) {
if (h <= 0 || b <= 0) {return(NA)} # h and b estimates must be > zero
if (c < 0) {return(NA)} # c must be positive
prop.exp = hassIIInr(X, b, c, h, T)/X
if(any(is.complex(prop.exp))){return(NA)} # Complex numbers don't help!
# The proportion consumed must be between 0 and 1 and not NaN
# If not then it must be bad estimate of a and h and should return NA
if(any(is.nan(prop.exp)) || any(is.na(prop.exp))){return(NA)}
if(any(prop.exp > 1) || any(prop.exp < 0)){return(NA)}
return(-sum(dbinom(Y, prob = prop.exp, size = X, log = TRUE)))
}
# hassIIInr_diff
# Model the difference between two groups (j) exposing a simple t-test on Da and Dh
# For further info see Juliano 2001, pg 193, eg. eq. 10.11
hassIIInr_diff <- function(X, grp, b, c, h, T, Db, Dc, Dh) {
# a <- ( b *X)/(1+ c *X) # From Hassell et al (1977)
a <- ((b-Db*grp)*X)/(1+(c-Dc*grp)*X)
# return(X-lamW::lambertW0(a* h *X*exp(-a*(T-h *X)))/(a* h))
return(X-lamW::lambertW0(a*(h-Dh*grp)*X*exp(-a*(T-(h-Dh*grp)*X)))/(a*(h-Dh*grp)))
}
# The NLL for the difference model... used by frair_compare()
hassIIInr_nll_diff <- function(b, c, h, T, Db, Dc, Dh, X, Y, grp) {
if (h <= 0 || b <= 0) {return(NA)} # h and b estimates must be > zero
if (c < 0) {return(NA)} # c must be positive
prop.exp = hassIIInr_diff(X, grp, b, c, h, T, Db, Dc, Dh)/X
if(any(is.complex(prop.exp))){return(NA)} # Complex numbers don't help!
# The proportion consumed must be between 0 and 1 and not NaN
# If not then it must be bad estimate of a and h and should return NA
if(any(is.nan(prop.exp)) || any(is.na(prop.exp))){return(NA)}
if(any(prop.exp > 1) || any(prop.exp < 0)){return(NA)}
return(-sum(dbinom(Y, prob = prop.exp, size = X, log = TRUE)))
}
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