Comparison of Two Measurement Methods Using Regression Analysis

A copy from mcr::mcreg in mcr package

Usage

mcreg(...)

Arguments

...

Arguments passed on to mcr::mcreg

x

measurement values of reference method, or two column matrix.

y

measurement values of test method.

error.ratio

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

alpha

value specifying the 100(1-alpha)% confidence level for confidence intervals (Default is 0.05).

mref.name

name of reference method (Default "Method1").

mtest.name

name of test Method (Default "Method2").

sample.names

names of cases (Default "S##").

method.reg

regression method. It is possible to choose between five regression methods: "LinReg" - ordinary least square regression.
"WLinReg" - weighted ordinary least square regression.
"Deming" - Deming regression.
"WDeming" - weighted Deming regression.
"TS" - Theil-Sen regression.
"PBequi" - equivariant Passing-Bablok regression.
"PaBa" - Passing-Bablok regression.
"PaBaLarge" - approximative Passing-Bablok regression for large datasets, operating on NBins classes of constant slope angle which each slope is classified to instead of building the complete triangular matrix of all N*N/2 slopes.

method.ci

method of confidence interval calculation. The function contains four basic methods for calculation of confidence intervals for regression coefficients. "analytical" - with parametric method.
"jackknife" - with leave one out resampling.
"bootstrap" - with ordinary non-parametric bootstrap resampling.
"nested bootstrap" - with ordinary non-parametric bootstrap resampling.

method.bootstrap.ci

bootstrap based confidence interval estimation method.

nsamples

number of bootstrap samples.

nnested

number of nested bootstrap samples.

rng.seed

integer number that sets the random number generator seed for bootstrap sampling. If set to NULL currently in the R session used RNG setting will be used.

rng.kind

type of random number generator for bootstrap sampling. Only used when rng.seed is specified, see set.seed for details.

iter.max

maximum number of iterations for weighted Deming iterative algorithm.

threshold

numerical tolerance for weighted Deming iterative algorithm convergence.

na.rm

remove measurement pairs that contain missing values (Default is FALSE).

NBins

number of bins used when 'reg.method="PaBaLarge"' to classify each slope in one of 'NBins' bins covering the range of all slopes

slope.measure

angular measure of pairwise slopes used for exact PaBa regression (see below for details).
"radian" - for data sets with even sample numbers median slope is calculated as average of two central slope angles.
"tangent" - for data sets with even sample numbers median slope is calculated as average of two central slopes (tan(angle)).

methodlarge

Boolean. This parameter applies only to regmethod="PBequi" and "TS". If TRUE, a quasilinear algorithm is used. If FALSE, a quadratic algorithm is used which is faster for less than several hundred data pairs.

Value

A regression fit model.

See also

mcr::mcreg()

Examples

data(platelet)
fit <- mcreg(
  x = platelet$Comparative, y = platelet$Candidate,
  method.reg = "Deming", method.ci = "jackknife"
)
#> Jackknife based calculation of standard error and confidence intervals according to Linnet's method.
printSummary(fit)
#> 
#> 
#> ------------------------------------------
#> 
#> Reference method: Method1
#> Test method:     Method2
#> Number of data points: 120
#> 
#> ------------------------------------------
#> 
#> The confidence intervals are calculated with jackknife (Linnet's) method.
#> Confidence level: 95%
#> Error ratio: 1
#> 
#> ------------------------------------------
#> 
#> DEMING REGRESSION FIT:
#> 
#>                EST          SE       LCI      UCI
#> Intercept 4.335885 1.568968372 1.2289002 7.442869
#> Slope     1.012951 0.009308835 0.9945175 1.031386
#> 
#> 
#> ------------------------------------------
#> 
#> JACKKNIFE SUMMARY
#> 
#>                EST Jack.Mean          Bias     Jack.SE
#> Intercept 4.335885  4.336377  4.918148e-04 1.568968372
#> Slope     1.012951  1.012950 -1.876312e-06 0.009308835
#> NULL
getCoefficients(fit)
#>                EST          SE       LCI      UCI
#> Intercept 4.335885 1.568968372 1.2289002 7.442869
#> Slope     1.012951 0.009308835 0.9945175 1.031386