diff options
Diffstat (limited to 'R/lod.R')
| -rw-r--r-- | R/lod.R | 26 |
1 files changed, 15 insertions, 11 deletions
@@ -1,23 +1,27 @@ -lod <- function(object, ..., alpha = 0.05, beta = 0.05, n = 1, w = "auto") +lod <- function(object, ..., alpha = 0.05, beta = 0.05) { UseMethod("lod") } -lod.default <- function(object, ..., alpha = 0.05, beta = 0.05, - n = 1, w = "auto") +lod.default <- function(object, ..., alpha = 0.05, beta = 0.05) { stop("lod is only implemented for univariate lm objects.") } -lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05, n = 1, w = "auto") +lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05) { - f <- function(x) { - y1 <- predict(object, data.frame(x = 0), interval="prediction", + y0 <- predict(object, data.frame(x = 0), interval="prediction", level = 1 - alpha) - y2 <- predict(object, data.frame(x = x), interval="prediction", - level = 1 - beta) - (y2[[1,"lwr"]] - y1[[1,"upr"]])^2 + yc <- y0[[1,"upr"]] + xc <- inverse.predict(object,yc)[["Prediction"]] + f <- function(x) + { + # Here I need the variance of y values as a function of x or + # y values + # Strangely, setting the confidence level to 0.5 does not result + # in a zero confidence or prediction interval } - tmp <- optimize(f,interval=c(0,max(object$model$x))) - return(tmp$minimum) + lod.x <- optimize(f,interval=c(0,max(object$model$x)))$minimum + lod.y <- predict(object, data.frame(x = lod.x)) + return(list(x = lod.x, y = lod.y)) } |