| lambdas {BivRegBLS} | R Documentation |
Measurement error variances ratio
Description
Calculate the measurement error variances ratio of two devices (Y over X): λ and λ_{XY}
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. |
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 λ and λ_{XY} are equal: λ is the ratio (Y over X) of the measurement error variances, while λ_{XY} 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 λ and λ_{XY} are also given.
Value
A lambdas class object, a table with 2 rows (λ and λ_{XY}) and their confidence intervals and pvalues in columns (the null hypothesized value is 1).
Author(s)
Bernard G FRANCQ
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
Examples
library(BivRegBLS)
data(SBP)
lambdas(data=SBP,xcol=c("J1","J2","J3"),ycol=8:10)