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`SS.RGBcalib` <-structure(function (Blev, Br, Bg, Bb, gamm, Rgun, Ggun, Bgun)
{
.expr1 <- Rgun^gamm
.expr4 <- Ggun^gamm
.expr7 <- Bgun^gamm
.expr10 <- ifelse(Rgun == 0, 0, log(Rgun))
.expr11 <- .expr1 * .expr10
.expr12 <- ifelse(Ggun == 0, 0, log(Ggun))
.expr13 <- .expr4 * .expr12
.expr14 <- ifelse(Bgun == 0, 0, log(Bgun))
.expr15 <- .expr7 * .expr14
.value <- Blev + Br * .expr1 + Bg * .expr4 + Bb * .expr7
.grad <- array(0, c(length(.value), 5), list(NULL, c("Blev",
"Br", "Bg", "Bb", "gamm")))
.hessian <- array(0, c(length(.value), 5, 5), list(NULL,
c("Blev", "Br", "Bg", "Bb", "gamm"), c("Blev", "Br",
"Bg", "Bb", "gamm")))
.grad[, "Blev"] <- 1
.grad[, "Br"] <- .expr1
.hessian[, "Br", "Br"] <- 0
.hessian[, "Br", "Bg"] <- .hessian[, "Bg", "Br"] <- 0
.hessian[, "Br", "Bb"] <- .hessian[, "Bb", "Br"] <- 0
.hessian[, "Br", "gamm"] <- .hessian[, "gamm", "Br"] <- .expr11
.grad[, "Bg"] <- .expr4
.hessian[, "Bg", "Bg"] <- 0
.hessian[, "Bg", "Bb"] <- .hessian[, "Bb", "Bg"] <- 0
.hessian[, "Bg", "gamm"] <- .hessian[, "gamm", "Bg"] <- .expr13
.grad[, "Bb"] <- .expr7
.hessian[, "Bb", "Bb"] <- 0
.hessian[, "Bb", "gamm"] <- .hessian[, "gamm", "Bb"] <- .expr15
.grad[, "gamm"] <- Br * .expr11 + Bg * .expr13 + Bb * .expr15
.hessian[, "gamm", "gamm"] <- Br * (.expr11 * .expr10) +
Bg * (.expr13 * .expr12) + Bb * (.expr15 * .expr14)
attr(.value, "gradient") <- .grad
attr(.value, "hessian") <- .hessian
.value
}
, initial = function(mCall, data, LHS, ...) {
Lum <- eval(asOneSidedFormula("Lum")[[2]], data)
Rgun <- eval(asOneSidedFormula("Rgun")[[2]], data)
Ggun <- eval(asOneSidedFormula("Ggun")[[2]], data)
Bgun <- eval(asOneSidedFormula("Bgun")[[2]], data)
Blev <- min(Lum)
Br <- max(Lum[Rgun > 0])
Bg <- max(Lum[Ggun > 0])
Bb <- max(Lum[Bgun > 0])
gamm <- 2.5
value <- c(Blev, Br, Bg, Bb, gamm)
names(value) <- mCall[c("Blev", "Br", "Bg", "Bb", "gamm")]
value
}
, pnames = c("Blev", "Br", "Bg", "Bb", "gamm"), class = "selfStart")
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