diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/inverse.predict.lm.R | 25 |
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)") { |