mc.paba.LargeData | R Documentation |
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.
mc.paba.LargeData(
X,
Y,
NBins = 1e+06,
alpha = 0.05,
posCor = TRUE,
calcCI = TRUE,
slope.measure = c("radian", "tangent")
)
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? |
slope.measure |
angular measure of pairwise slopes (see |
Matrix of estimates and confidence intervals for intercept and slope. No standard errors provided by this algorithm.
Andre Schuetzenmeister andre.schuetzenmeister@roche.com (partly re-using code of function 'mc.paba')
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)
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