From 73e650114af77582238abf5273e63005e0b2287e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 6 Mar 2017 17:00:48 +0100 Subject: Static documentation now built by pkgdown::build_site() --- docs/inverse.predict.html | 204 ---------------------------------------------- 1 file changed, 204 deletions(-) delete mode 100644 docs/inverse.predict.html (limited to 'docs/inverse.predict.html') diff --git a/docs/inverse.predict.html b/docs/inverse.predict.html deleted file mode 100644 index 4d66549..0000000 --- a/docs/inverse.predict.html +++ /dev/null @@ -1,204 +0,0 @@ - - - - -inverse.predict. chemCal 0.1-37 - - - - - - - - - - - - - - - - - - -
-
- -
- -

Predict x from y for a linear calibration

- -
-
-

Usage

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

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. -
-
- -
-

Value

- -

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

- -
- -
-

Description

- -

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.

- -
- -
-

Note

- -

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

- -
- -
-

References

- -

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

- -
- -

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 - -$Confidence -[1] 4.906751 - -$`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 - -
-
- -
- - -
- - \ No newline at end of file -- cgit v1.2.1