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# This is an implementation of Equation (8.28) in the Handbook of Chemometrics
# and Qualimetrics, Part A, Massart et al (1997), page 200, validated with
# Example 8 on the same page, extended as specified in the package vignette
inverse.predict <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, var.s = "auto")
{
UseMethod("inverse.predict")
}
inverse.predict.default <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, var.s = "auto")
{
stop("Inverse prediction only implemented for univariate lm objects.")
}
inverse.predict.lm <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, var.s = "auto")
{
yname = names(object$model)[[1]]
xname = names(object$model)[[2]]
if (ws == "auto") {
ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
}
if (length(object$weights) > 0) {
wx <- split(object$weights,object$model[[xname]])
w <- sapply(wx,mean)
} else {
w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
}
.inverse.predict(object = object, newdata = newdata,
ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
}
inverse.predict.rlm <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, var.s = "auto")
{
yname = names(object$model)[[1]]
xname = names(object$model)[[2]]
if (ws == "auto") {
ws <- mean(object$w)
}
wx <- split(object$weights,object$model[[xname]])
w <- sapply(wx,mean)
.inverse.predict(object = object, newdata = newdata,
ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
}
.inverse.predict <- function(object, newdata, ws, alpha, var.s, w, xname, yname)
{
if (length(object$coef) > 2)
stop("More than one independent variable in your model - not implemented")
if (alpha <= 0 | alpha >= 1)
stop("Alpha should be between 0 and 1 (exclusive)")
ybars <- mean(newdata)
m <- length(newdata)
yx <- split(object$model[[yname]], object$model[[xname]])
n <- length(yx)
df <- n - length(object$coef)
x <- as.numeric(names(yx))
ybar <- sapply(yx,mean)
yhatx <- split(object$fitted.values, object$model[[xname]])
yhat <- sapply(yhatx, mean)
se <- sqrt(sum(w * (ybar - yhat)^2)/df)
if (var.s == "auto") {
var.s <- se^2/ws
}
b1 <- object$coef[[xname]]
ybarw <- sum(w * ybar)/sum(w)
# This is the adapted form of equation 8.28 (see package vignette)
sxhats <- 1/b1 * sqrt(
(var.s / m) +
se^2 * (1/sum(w) +
(ybars - ybarw)^2 * sum(w) /
(b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2)))
)
if (names(object$coef)[1] == "(Intercept)") {
b0 <- object$coef[["(Intercept)"]]
} else {
b0 <- 0
}
xs <- (ybars - b0) / b1
t <- qt(1-0.5*alpha, n - 2)
conf <- t * sxhats
result <- list("Prediction"=xs,"Standard Error"=sxhats,
"Confidence"=conf, "Confidence Limits"=c(xs - conf, xs + conf))
return(result)
}
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