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| \name{PBreg}
\alias{PBreg}
\alias{print.PBreg}
\title{Passing-Bablok regression}
\description{
Implementation of the Passing-Bablok's procedure for assessing of
the equality of measurements by two different analytical methods.
}
\usage{
PBreg(x, y=NULL, conf.level=0.05, wh.meth=1:2)
\method{print}{PBreg}(x,...)
}
\arguments{
\item{x}{a numeric vector of measurements by method A, alternatively
a data frame of exactly two columns, first column with measurements
by method A, second column with measurements by method B. If \code{x} is a
\code{\link{Meth}} object, the methods from that are used in the regression.}
\item{y}{a numeric vector of measurements by method B - must be of
the same length as \code{x}. If not provided, \code{x} must be
a data frame of exactly 2 columns.}
\item{conf.level}{confidence level for calculation of confidence
boundaries.}
\item{wh.meth}{Which of the methods from the \code{Meth} object are used in
the regression.}
\item{\ldots}{other parameters, currently ignored.}
}
\details{
This is an implementation of the original Passing-Bablok
procedure of fitting unbiased linear regression line to data in the
method comparison studies. It calcualtes the unbiased slope and intercept,
along with their confidence intervals. However, the tests for
linearity is not yet fully implemented.
It doesn't matter which results are assigned to "Method A" and
"Method B", however the "Method A" results will be plotted on the
x-axis by the \code{plot} method.
}
\note{
Please note that this method can become very computationally
intensive for larger numbers of observations. One can expect a reasonable
computation times for datasets with fewer than 100 observations.
}
\value{
\code{PBreg} returns an object of class \code{"PBreg"}, for which
the \code{print} and \code{plot} methods are defined.
An object of class \code{"PBreg"} is a list composed of the following
elements:
\item{coefficients}{a matrix of 3 columns and 2 rows, containing the
estimates of the intercept and slope, along with their confidence
boundaries.}
\item{residuals}{defined as in the \code{"lm"} class, as the response
minus the fitted value.}
\item{fitted.values}{the fitted values.}
\item{model}{the model data frame used.}
\item{n}{a vector of two values: the number of observations read, and
the number of observations used.}
\item{S}{A vector of all slope estimates.}
\item{adj}{A vector of fit parameters, where \emph{Ss} is the number of
estimated slopes (\code{length(S)}), \emph{K} is the offset for negative
slopes, \emph{M1} and \emph{M2} are the locations of confidence boundaries
in \code{S}, and \emph{l} and \emph{L} are the numbers of points above and
below the fitted line, used in cusum calculation.}
\item{cusum}{A vector of cumulative sums of residuals sorted by the D-rank.}
\item{Di}{A vector of D-ranks.}
}
\references{Passing, H. and Bablok, W. (1983), A New Biometrical Procedure
for Testing the Equality of Measurements from Two Different Analytical
Methods. \emph{Journal of Clinical Chemistry and Clinical Biochemistry},
\bold{Vol 21}, 709--720}
\author{Michal J. Figurski \email{mfigrs@gmail.com}}
\seealso{
\code{\link{plot.PBreg}, \link{Deming}}.
}
\examples{
## Model data frame generation
a <- data.frame(x=seq(1, 30)+rnorm(mean=0, sd=1, n=30),
y=seq(1, 30)*rnorm(mean=1, sd=0.4, n=30))
## Call to PBreg
x <- PBreg(a)
print(x)
par(mfrow=c(2,2))
plot(x, s=1:4)
# A real data example
data(milk)
milk <- Meth(milk)
summary(milk)
PBmilk <- PBreg( milk )
plot( PBmilk )
} |
