BivRegBLS (version 1.1.1)

CBLS.fit: Fit a Correlated Bivariate Least Square regression (CBLS): estimates table

Description

Estimate the Correlated Bivariate Least Square regression with replicated data in a (M,D) plot (Bland-Altman) where M=(X+Y)/2 and D=Y-X, provide the estimates table.

Usage

CBLS.fit(data = NULL, xcol = 1, ycol = 2, var.x = NULL, var.y = NULL,
     ratio.var = NULL, conf.level = 0.95)

Arguments

data

a data set (data frame or matrix).

xcol

a numeric vector to specify the X column(s) or a character vector with the column names.

ycol

a numeric vector to specify the Y column(s) or a character vector with the column names.

var.x

a numeric variable for the variance of the measurement error of device X if known.

var.y

a numeric variable for the variance of the measurement error of device Y if known.

ratio.var

a numeric value for , the ratio of the measurement error variances (Y over X) if known.

conf.level

a numeric value for the confidence level (expressed between 0 and 1).

Value

A table with the estimates of the intercept and the slope, standard error, confidence interval and pvalue (null hypothesis: slope = 0, intercept = 0).

Details

The data argument is mandatory. If the data are unreplicated, then the measurement error variances must be given or their ratio () in order to calculate the correlation, , between the measurement errors of the differences (on the Y-axis) and the measurement errors of the means (on the X-axis). The confidence level is used for the confidence intervals of the parameters (, (slope), (intercept)).

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.

See Also

BLS, CBLS

Examples

# NOT RUN {
library(BivRegBLS)
data(SBP)
# CBLS regression on replicated data
res1=CBLS.fit(data=SBP,xcol=c("J1","J2","J3"),ycol=8:10)
# CBLS regression on unreplicated data with measurement error variances previously estimated
res2=CBLS.fit(data=SBP,xcol=c("J1"),ycol="S1",var.x=80,var.y=50)
# }