R version 3.1.0 (2014-04-10) -- "Spring Dance"
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> require(chemCal)
Loading required package: chemCal
> data(massart97ex1)
> m <- lm(y ~ x, data = massart97ex1)
> inverse.predict(m, 15)        #  6.1 +- 4.9
$Prediction
[1] 6.09381

$`Standard Error`
[1] 1.767278

$Confidence
[1] 4.906751

$`Confidence Limits`
[1]  1.187059 11.000561

> inverse.predict(m, 90)        # 43.9 +- 4.9
$Prediction
[1] 43.93983

$`Standard Error`
[1] 1.767747

$Confidence
[1] 4.908053

$`Confidence Limits`
[1] 39.03178 48.84788

> inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
$Prediction
[1] 43.93983

$`Standard Error`
[1] 1.141204

$Confidence
[1] 3.168489

$`Confidence Limits`
[1] 40.77134 47.10832

> 
> data(massart97ex3)
> attach(massart97ex3)
> yx <- split(y, x)
> ybar <- sapply(yx, mean)
> s <- round(sapply(yx, sd), digits = 2)
> w <- round(1 / (s^2), digits = 3)
> weights <- w[factor(x)]
> m <- lm(y ~ x, w = weights)
> #calplot(m)
> 
> inverse.predict(m, 15, ws = 1.67)  # 5.9 +- 2.5
$Prediction
[1] 5.865367

$`Standard Error`
[1] 0.8926109

$Confidence
[1] 2.478285

$`Confidence Limits`
[1] 3.387082 8.343652

> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9
$Prediction
[1] 44.06025

$`Standard Error`
[1] 2.829162

$Confidence
[1] 7.855012

$`Confidence Limits`
[1] 36.20523 51.91526

> 
> m0 <- lm(y ~ x) 
> lod(m0)
$x
[1] 5.407085

$y
       1 
13.63911 

> 
> loq(m0)
$x
[1] 13.97764

$y
      1 
30.6235 

> loq(m, w.loq = 1.67)
$x
[1] 7.346195

$y
       1 
17.90777 

> 
> proc.time()
   user  system elapsed 
  0.529   0.327   0.443