BivRegBLS (version 1.1.1)

lambdas: Measurement error variances ratio

Description

Calculate the measurement error variances ratio of two devices (Y over X): and

Usage

lambdas(data = NULL, xcol = NULL, ycol = NULL, conf.level = 0.95)

Arguments

data

a data set (data frame or matrix).

xcol

a numeric vector to specify the X columns or a character vector with the column names.

ycol

a numeric vector to specify the Y columns or a character vector with the column names.

conf.level

a numeric value for the confidence level.

Value

A lambdas class object, a table with 2 rows ( and ) and their confidence intervals and pvalues in columns (the null hypothesized value is 1).

Details

The data must be replicated to estimate the measurement error variances. If the number of replicates in X is equal to the number of replicates in Y, then and are equal: is the ratio (Y over X) of the measurement error variances, while is similar but takes also into account the number of replicates per device (nx and ny). Unbiased estimators (which is not the ratio of the two variances) for and are also given.

References

Francq BG, Govaerts BB. How to regress and predict in a Bland-Altman plot? Review and contribution based on tolerance intervals and correlated-errors-in-variables models. Statistics in Medicine, 2016; 35:2328-2358. Francq BG, Govaerts BB. Measurement methods comparison with errors-in-variables regressions. From horizontal to vertical OLS regression, review and new perspectives. Chemometrics and Intelligent Laboratory Systems, 2014; 134:123-139.

See Also

desc.stat

Examples

# NOT RUN {
library(BivRegBLS)
data(SBP)
lambdas(data=SBP,xcol=c("J1","J2","J3"),ycol=8:10)
# }