#Yield-Loss function (rectangular hyperbola) ######################
YL.fun <- function(predictor, i, A) {
i * predictor/(1 + i/A * predictor)
}
YL.Init <- function(mCall, LHS, data, ...) {
xy <- sortedXyData(mCall[["predictor"]], LHS, data)
x <- xy[, "x"]; y <- xy[, "y"]
pseudoX <- 1 / x[x > 0]; pseudoY <- 1 / y[x > 0]
lmFit <- lm(pseudoY ~ pseudoX)
coefs <- coef(lmFit)
A <- 1 / coefs[1]
i <- 1 / coefs[2]
value <- c(i, A)
names(value) <- mCall[c("i", "A")]
value
}
NLS.YL <- selfStart(YL.fun, YL.Init, parameters=c("i", "A"))
"DRC.YL" <- function(fixed = c(NA, NA), names = c("i", "A")) {
## Checking arguments
numParm <- 2
if (!is.character(names) | !(length(names) == numParm)) {stop("Not correct 'names' argument")}
if (!(length(fixed) == numParm)) {stop("Not correct 'fixed' argument")}
## Fixing parameters (using argument 'fixed')
notFixed <- is.na(fixed)
parmVec <- rep(0, numParm)
parmVec[!notFixed] <- fixed[!notFixed]
## Defining the non-linear function
fct <- function(x, parm) {
parmMat <- matrix(parmVec, nrow(parm), numParm, byrow = TRUE)
parmMat[, notFixed] <- parm
i <- parmMat[, 1]; A <- parmMat[, 2]
YL.fun(x, i, A)
}
## Defining self starter function
ssfct <- function(dataf) {
x <- dataf[, 1]
y <- dataf[, 2]
#regression on pseudo y values
pseudoY <- 1 / y[x > 0]
pseudoX <- 1 / x [x > 0]
coefs <- coef( lm(pseudoY ~ pseudoX) )
A <- 1 / coefs[1]; i <- 1 / coefs[2]
return(c(i, A)[notFixed])
}
## Defining names
pnames <- names[notFixed]
## Defining derivatives
## Defining the ED function
## Defining the inverse function
## Defining descriptive text
text <- "Yield-Loss function (Cousens, 1985)"
## Returning the function with self starter and names
returnList <- list(fct = fct, ssfct = ssfct, names = pnames, text = text, noParm = sum(is.na(fixed)))
class(returnList) <- "drcMean"
invisible(returnList)
}
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