1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
|
loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
var.loq = "auto", tol = "default")
{
UseMethod("loq")
}
loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
var.loq = "auto", tol = "default")
{
stop("loq is only implemented for univariate lm objects.")
}
loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w.loq = "auto",
var.loq = "auto", tol = "default")
{
if (length(object$weights) > 0 && var.loq == "auto" && w.loq == "auto") {
stop(paste("If you are using a model from weighted regression,",
"you need to specify a reasonable approximation for the",
"weight (w.loq) or the variance (var.loq) at the",
"limit of quantification"))
}
xname <- names(object$model)[[2]]
xvalues <- object$model[[2]]
yname <- names(object$model)[[1]]
f <- function(x) {
newdata <- data.frame(x = x)
names(newdata) <- xname
y <- predict(object, newdata)
p <- inverse.predict(object, rep(y, n), ws = w.loq,
var.s = var.loq, alpha = alpha)
(p[["Prediction"]] - k * p[["Confidence"]])^2
}
if (tol == "default") tol = min(xvalues[xvalues !=0]) / 1000
loq.x <- optimize(f, interval = c(0, max(xvalues) * 10), tol = tol)$minimum
newdata <- data.frame(x = loq.x)
names(newdata) <- xname
loq.y <- predict(object, newdata)
loq <- list(loq.x, loq.y)
names(loq) <- c(xname, yname)
return(loq)
}
|
