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-rw-r--r--R/inverse.predict.lm.R25
1 files changed, 11 insertions, 14 deletions
diff --git a/R/inverse.predict.lm.R b/R/inverse.predict.lm.R
index 927e672..77d548f 100644
--- a/R/inverse.predict.lm.R
+++ b/R/inverse.predict.lm.R
@@ -23,10 +23,9 @@ inverse.predict.lm <- function(object, newdata, ...,
ws <- ifelse(length(object$weights) > 0, mean(object$weights), 1)
}
if (length(object$weights) > 0) {
- wx <- split(object$weights,object$model[[xname]])
- w <- sapply(wx,mean)
+ w <- object$weights
} else {
- w <- rep(1,length(split(object$model[[yname]],object$model[[xname]])))
+ w <- rep(1, nrow(object$model))
}
.inverse.predict(object = object, newdata = newdata,
ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
@@ -40,8 +39,7 @@ inverse.predict.rlm <- function(object, newdata, ...,
if (ws == "auto") {
ws <- mean(object$w)
}
- wx <- split(object$weights,object$model[[xname]])
- w <- sapply(wx,mean)
+ w <- object$w
.inverse.predict(object = object, newdata = newdata,
ws = ws, alpha = alpha, var.s = var.s, w = w, xname = xname, yname = yname)
}
@@ -57,14 +55,13 @@ inverse.predict.rlm <- function(object, newdata, ...,
ybars <- mean(newdata)
m <- length(newdata)
- yx <- split(object$model[[yname]], object$model[[xname]])
- n <- length(yx)
+ n <- nrow(object$model)
df <- n - length(object$coef)
- x <- as.numeric(names(yx))
- ybar <- sapply(yx,mean)
- yhatx <- split(object$fitted.values, object$model[[xname]])
- yhat <- sapply(yhatx, mean)
- se <- sqrt(sum(w * (ybar - yhat)^2)/df)
+ xi <- object$model[[2]]
+ yi <- object$model[[1]]
+ yihat <- object$fitted.values
+
+ se <- sqrt(sum(w * (yi - yihat)^2)/df)
if (var.s == "auto") {
var.s <- se^2/ws
@@ -72,14 +69,14 @@ inverse.predict.rlm <- function(object, newdata, ...,
b1 <- object$coef[[xname]]
- ybarw <- sum(w * ybar)/sum(w)
+ ybarw <- sum(w * yi)/sum(w)
# This is the adapted form of equation 8.28 (see package vignette)
sxhats <- 1/b1 * sqrt(
(var.s / m) +
se^2 * (1/sum(w) +
(ybars - ybarw)^2 * sum(w) /
- (b1^2 * (sum(w) * sum(w * x^2) - sum(w * x)^2)))
+ (b1^2 * (sum(w) * sum(w * xi^2) - sum(w * xi)^2)))
)
if (names(object$coef)[1] == "(Intercept)") {

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