diff options
Diffstat (limited to 'inst/doc/chemCal.Rnw')
| -rw-r--r-- | inst/doc/chemCal.Rnw | 13 |
1 files changed, 6 insertions, 7 deletions
diff --git a/inst/doc/chemCal.Rnw b/inst/doc/chemCal.Rnw index 26b224f..77888b4 100644 --- a/inst/doc/chemCal.Rnw +++ b/inst/doc/chemCal.Rnw @@ -19,7 +19,7 @@ Ron Wehrens led to the belief that it could be heavily improved if the inverse prediction method given in \cite{massart97} would be implemented, since it also covers the case of weighted regression. -At the moment, the package only consists of two functions, working +At the moment, the package consists of three functions, working on univariate linear models of class \texttt{lm} or \texttt{rlm}. When calibrating an analytical method, the first task is to generate @@ -27,6 +27,10 @@ a suitable model. If we want to use the \chemCal{} functions, we will have to restrict ourselves to univariate, possibly weighted, linear regression so far. +For the weighted case, the function \code{predict.lm} had to be +rewritten, in order to allow for weights for the x values used to +predict the y values. + Once such a model has been created, the calibration can be graphically shown by using the \texttt{calplot} function: @@ -34,12 +38,7 @@ shown by using the \texttt{calplot} function: library(chemCal) data(massart97ex3) attach(massart97ex3) -yx <- split(y,factor(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) +m <- lm(y ~ x, w = rep(0.01,length(x))) calplot(m) @ |