mc.mmNgdemingConstCV {mcrPioda}

Calculate MM Deming Regression

Description

Calculate MM Deming regression with iterative algorithm inspired on the work of Linnet. The algorithm uses bisquare redescending weights. For maximal stability and convergence the euclidean residuals are scaled in each iteration with a fresh calculated MAD instead of keeping the same MAD (assessed at the starting step) for the whole iteration. This algorithm is available only for positive values. But even in this case there is no guarantee that the algorithm always converges.

Usage

mc.mmNgdemingConstCV(
  X,
  Y,
  error.ratio,
  iter.max = 30,
  threshold = 1e-06,
  kM = 1.345,
  tauMM = 4.685,
  bdPoint = 0.5,
  priorSlope = 1,
  priorIntercept = 0
)

Arguments

X

measurement values of reference method.

Y

measurement values of test method.

error.ratio

ratio between squared measurement errors of reference- and test method, necessary for Deming regression (Default is 1).

iter.max

maximal number of iterations.

threshold

threshold value.

kM

Huber's k for the M weighting, default kM = 1.345

tauMM

Tukey's tau for bisquare redescending weighting function, default tauMM = 4,685

bdPoint

Proportion of data points selected for the highly robust M regression used for the determination of the starting parameters. Default 0.5

priorSlope

starting slope value for PiMMDeming, default priorSlope = 1

priorIntercept

starting intercept value for PiMMDeming, default priorIntercept = 0

Value

a list with elements

b0

intercept.

b1

slope.

xw

average of reference method values.

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).