BivRegBLS (version 1.1.1)

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

Description

Estimate the (homoscedastic) Bivariate Least Square regression with unreplicated or replicated data: provide the estimates table

Usage

BLS.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 = 1, intercept = 0).

Details

The data argument is mandatory. If the data are unreplicated, then the measurement error variances must be given or their ratio (). 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. 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. Francq BG, Govaerts BB. Hyperbolic confidence bands of errors-in-variables regression lines applied to method comparison studies. Journal de la Societe Francaise de Statistique 2014; 155(1):23-45.

See Also

BLS, CBLS

Examples

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