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-rw-r--r--R/calplot.R11
-rw-r--r--R/inverse.predict.lm.R22
-rw-r--r--R/lod.R23
-rw-r--r--R/loq.R2
4 files changed, 35 insertions, 23 deletions
diff --git a/R/calplot.R b/R/calplot.R
index feb9727..2deed5a 100644
--- a/R/calplot.R
+++ b/R/calplot.R
@@ -21,9 +21,10 @@ calplot.lm <- function(object, xlim = "auto", ylim = "auto",
m <- object
level <- 1 - alpha
- x <- m$model$x
- y <- m$model$y
- newdata <- data.frame(x = seq(0,max(x),length=250))
+ y <- m$model[[1]]
+ x <- m$model[[2]]
+ newdata <- list(x = seq(0,max(x),length=250))
+ names(newdata) <- names(m$model)[[2]]
pred.lim <- predict(m, newdata, interval = "prediction",level=level)
conf.lim <- predict(m, newdata, interval = "confidence",level=level)
if (xlim == "auto") xlim = c(0,max(x))
@@ -36,9 +37,9 @@ calplot.lm <- function(object, xlim = "auto", ylim = "auto",
ylim = ylim
)
points(x,y, pch = 21, bg = "yellow")
- matlines(newdata$x, pred.lim, lty = c(1, 4, 4),
+ matlines(newdata[[1]], pred.lim, lty = c(1, 4, 4),
col = c("black", "red", "red"))
- matlines(newdata$x, conf.lim, lty = c(1, 3, 3),
+ matlines(newdata[[1]], conf.lim, lty = c(1, 3, 3),
col = c("black", "green4", "green4"))
legend(min(x),
diff --git a/R/inverse.predict.lm.R b/R/inverse.predict.lm.R
index 2ead9fb..e5f014c 100644
--- a/R/inverse.predict.lm.R
+++ b/R/inverse.predict.lm.R
@@ -17,32 +17,36 @@ inverse.predict.default <- function(object, newdata, ...,
inverse.predict.lm <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, ss = "auto")
{
+ yname = names(object$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$x)
+ wx <- split(object$weights,object$model[[xname]])
w <- sapply(wx,mean)
} else {
- w <- rep(1,length(split(object$model$y,object$model$x)))
+ w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
}
.inverse.predict(object = object, newdata = newdata,
- ws = ws, alpha = alpha, ss = ss, w = w)
+ ws = ws, alpha = alpha, ss = ss, w = w, xname = xname, yname = yname)
}
inverse.predict.rlm <- function(object, newdata, ...,
ws = "auto", alpha = 0.05, ss = "auto")
{
+ yname = names(object$model)[[1]]
+ xname = names(object$model)[[2]]
if (ws == "auto") {
ws <- mean(object$w)
}
- wx <- split(object$weights,object$model$x)
+ wx <- split(object$weights,object$model[[xname]])
w <- sapply(wx,mean)
.inverse.predict(object = object, newdata = newdata,
- ws = ws, alpha = alpha, ss = ss, w = w)
+ ws = ws, alpha = alpha, ss = ss, w = w, xname = xname, yname = yname)
}
-.inverse.predict <- function(object, newdata, ws, alpha, ss, w)
+.inverse.predict <- function(object, newdata, ws, alpha, ss, w, xname, yname)
{
if (length(object$coef) > 2)
stop("More than one independent variable in your model - not implemented")
@@ -53,12 +57,12 @@ inverse.predict.rlm <- function(object, newdata, ...,
ybars <- mean(newdata)
m <- length(newdata)
- yx <- split(object$model$y,object$model$x)
+ yx <- split(object$model[[yname]],object$model[[xname]])
n <- length(yx)
df <- n - length(objects$coef)
x <- as.numeric(names(yx))
ybar <- sapply(yx,mean)
- yhatx <- split(object$fitted.values,object$model$x)
+ yhatx <- split(object$fitted.values,object$model[[xname]])
yhat <- sapply(yhatx,mean)
se <- sqrt(sum(w*(ybar - yhat)^2)/df)
if (ss == "auto") {
@@ -67,7 +71,7 @@ inverse.predict.rlm <- function(object, newdata, ...,
ss <- ss
}
- b1 <- object$coef[["x"]]
+ b1 <- object$coef[[xname]]
ybarw <- sum(w * ybar)/sum(w)
diff --git a/R/lod.R b/R/lod.R
index 1bb7981..73f5353 100644
--- a/R/lod.R
+++ b/R/lod.R
@@ -10,18 +10,25 @@ lod.default <- function(object, ..., alpha = 0.05, beta = 0.05)
lod.lm <- function(object, ..., alpha = 0.05, beta = 0.05)
{
- y0 <- predict(object, data.frame(x = 0), interval="prediction",
- level = 1 - alpha)
+ xname <- names(object$model)[[2]]
+ newdata <- data.frame(0)
+ names(newdata) <- xname
+ y0 <- predict(object, newdata, interval="prediction",
+ level = 1 - 2 * alpha )
yc <- y0[[1,"upr"]]
xc <- inverse.predict(object,yc)[["Prediction"]]
f <- function(x)
{
- # Here I need the variance of y values as a function of x or
- # y values
- # Strangely, setting the confidence level to 0.5 does not result
- # in a zero confidence or prediction interval
+ newdata <- data.frame(x)
+ names(newdata) <- xname
+ pi.y <- predict(object, newdata, interval = "prediction",
+ level = 1 - 2 * beta)
+ yd <- pi.y[[1,"lwr"]]
+ (yd - yc)^2
}
- lod.x <- optimize(f,interval=c(0,max(object$model$x)))$minimum
- lod.y <- predict(object, data.frame(x = lod.x))
+ lod.x <- optimize(f,interval=c(0,max(object$model[[xname]])))$minimum
+ newdata <- data.frame(x = lod.x)
+ names(lod.x) <- xname
+ lod.y <- predict(object, data.frame(lod.x))
return(list(x = lod.x, y = lod.y))
}
diff --git a/R/loq.R b/R/loq.R
index 33e9556..c493a64 100644
--- a/R/loq.R
+++ b/R/loq.R
@@ -5,7 +5,7 @@ loq <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
loq.default <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")
{
- stop("lod is only implemented for univariate lm objects.")
+ stop("loq is only implemented for univariate lm objects.")
}
loq.lm <- function(object, ..., alpha = 0.05, k = 3, n = 1, w = "auto")

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