inverse.predict. chemCal 0.1-37

Usage

inverse.predict(object, newdata, ...,
-  ws, alpha=0.05, var.s = "auto")

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Arguments

object: - A univariate model object of class lm or - rlm - with model formula y ~ x or y ~ x - 1. -
newdata: - A vector of observed y values for one sample. -
...: - Placeholder for further arguments that might be needed by - future implementations. -
ws: - The weight attributed to the sample. This argument is obligatory - if object has weights. -
alpha: - The error tolerance level for the confidence interval to be reported. -
var.s: - The estimated variance of the sample measurements. The default is to take - the residual standard error from the calibration and to adjust it - using ws, if applicable. This means that var.s - overrides ws. -

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Value

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- A list containing the predicted x value, its standard error and a - confidence interval. -

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Description

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This function predicts x values using a univariate linear model that has been - generated for the purpose of calibrating a measurement method. Prediction - intervals are given at the specified confidence level. - The calculation method was taken from Massart et al. (1997). In particular, - Equations 8.26 and 8.28 were combined in order to yield a general treatment - of inverse prediction for univariate linear models, taking into account - weights that have been used to create the linear model, and at the same - time providing the possibility to specify a precision in sample measurements - differing from the precision in standard samples used for the calibration. - This is elaborated in the package vignette.

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Note

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The function was validated with examples 7 and 8 from Massart et al. (1997).

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References

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Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., - Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, - p. 200

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Examples

# This is example 7 from Chapter 8 in Massart et al. (1997)
-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
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-$Confidence
-[1] 4.906751
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-$`Confidence Limits`
-[1]  1.187059 11.000561
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-
-inverse.predict(m, 90)        # 43.9 +- 4.9
-$Prediction
-[1] 43.93983
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-$`Standard Error`
-[1] 1.767747
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-$Confidence
-[1] 4.908053
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-$`Confidence Limits`
-[1] 39.03178 48.84788
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-
-inverse.predict(m, rep(90,5)) # 43.9 +- 3.2
-$Prediction
-[1] 43.93983
-
-$`Standard Error`
-[1] 1.141204
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-$Confidence
-[1] 3.168489
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-$`Confidence Limits`
-[1] 40.77134 47.10832
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-

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