Learn R Programming

mcr (version 1.2.2)

mc.paba.LargeData: Passing-Bablok Regression for Large Datasets

Description

This function represents an interface to a fast C-implementation of an adaption of the Passing-Bablok algorithm for large datasets. Instead of building the complete matrix of pair-wise slope values, a pre-defined binning of slope-values is used (Default NBins=1e06). This reduces the required memory dramatically and speeds up the computation.

Usage

mc.paba.LargeData(X, Y, NBins = 1e+06, alpha = 0.05, posCor = TRUE,
  calcCI = TRUE)

Value

Matrix of estimates and confidence intervals for intercept and slope. No standard errors provided by this algorithm.

Arguments

X

(numeric) vector containing measurement values of reference method

Y

(numeric) vector containing measurement values of test method

NBins

(integer) value specifying the number of bins used to classify slope-values

alpha

(numeric) value specifying the 100(1-alpha)% confidence level for confidence intervals

posCor

(logical) should algorithm assume positive correlation, i.e. symmetry around slope 1?

calcCI

(logical) should confidence intervals be computed?

Author

Andre Schuetzenmeister andre.schuetzenmeister@roche.com (partly re-using code of function 'mc.paba')

Examples

Run this code
library("mcr")
 data(creatinine,package="mcr")

# remove any NAs
crea <- na.omit(creatinine)

# call the approximative Passing-Bablok algorithm (Default NBins=1e06)
res1 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical")
getCoefficients(res1)

# now increase the number of bins and see whether this makes a difference
res2 <- mcreg(x=crea[,1], y=crea[,2], method.reg="PaBaLarge", method.ci="analytical", NBins=1e07)
getCoefficients(res2)
getCoefficients(res1)-getCoefficients(res2)

Run the code above in your browser using DataLab