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###############################################################################
##
## mcWDeming.r
##
## Function for computing weighted deming regression for two methods with proportional errors.
##
## Copyright (C) 2011 Roche Diagnostics GmbH
##
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
##
###############################################################################
#' Calculate Weighted Deming Regression
#'
#' Calculate weighted deming regression with iterative algorithm suggested by Linnet.
#' This algorithm is avalaible only for positive values. But even in this case there is no guarantee that
#' the algorithm always converges.
#'
#' @param X measurement values of reference method.
#' @param Y measurement values of test method.
#' @param error.ratio ratio between squared measurement errors of reference- and test method, necessary for Deming regression (Default is 1).
#' @param iter.max maximal number of iterations.
#' @param threshold threshold value.
#' @return a list with elements
#' \item{b0}{intercept.}
#' \item{b1}{slope.}
#' \item{xw}{average of reference method values.}
#' \item{iter}{number of iterations.}
#' @references Linnet K.
#' Evaluation of Regression Procedures for Methods Comparison Studies.
#' CLIN. CHEM. 39/3, 424-432 (1993).
#'
#' Linnet K.
#' Estimation of the Linear Relationship between the Measurements of two Methods with Proportional Errors.
#' STATISTICS IN MEDICINE, Vol. 9, 1463-1473 (1990).
mc.wdemingConstCV <- function(X, Y, error.ratio, iter.max = 30, threshold = 0.000001)
{
# Check validity of parameters
stopifnot(is.numeric(X))
stopifnot(is.numeric(Y))
stopifnot(length(X) == length(Y))
stopifnot(is.numeric(error.ratio))
stopifnot(error.ratio > 0)
stopifnot(is.numeric(iter.max))
stopifnot(round(iter.max) == iter.max)
stopifnot(iter.max > 0)
stopifnot(is.numeric(threshold))
stopifnot(threshold >= 0)
# This algorithm often doesn't converge if there are negative
# measurements in data set
if (min(X) < 0 | min(Y) < 0){
return(paste("There are negative values in data set."))
}else{
######################################################
### call C-function
intercept <- slope <- seIntercept <- seSlope <- 0
xw <- 0
maxit <- iter.max
nX <- length(X)
### mode = 0 - Deming regression
### mode = 1 - WDeming regression
mode <- 1
W <- rep(1, nX)
model.Deming <- .C("calc_Deming",
x = as.numeric(X), y = as.numeric(Y),
n = as.integer(nX),
error_ratio = as.numeric(error.ratio),
intercept = as.numeric(intercept),
slope = as.numeric(slope),
seIntercept = as.numeric(seIntercept),
seSlope = as.numeric(seSlope),
mode = as.integer(mode),
maxit = as.integer(iter.max),
threshold = as.numeric(threshold),
W = as.numeric(W),
xw = as.numeric(xw),
PACKAGE="mcr")
if (model.Deming$maxit >= maxit) {
warning(paste("no konvergence after", maxit, "iterations"))
}
list(b1 = model.Deming$slope, b0 = model.Deming$intercept,
iter = model.Deming$maxit,
xw = model.Deming$xw, weight = model.Deming$W)
}
}
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