CBLS.fit {BivRegBLS}

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 λ, 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).

Details

The data argument is mandatory. If the data are unreplicated, then the measurement error variances must be given or their ratio (λ) in order to calculate the correlation, ρ_{MD}, 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 (ρ_{MD}, β (slope), α (intercept)).

Value

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

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.

See Also

BLS, CBLS

Examples

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)