BivRegBLS (version 1.1.1)

XY.plot: Display the BLS regression in a (X,Y) plot

Description

Display the BLS regression in a (X,Y) plot with or without hyperbolic confidence and/or predictive intervals, and an acceptance interval.

Usage

XY.plot(BLS.results = NULL, xname = "X", yname = "Y", antilog = NULL,
        accept.int = 0, accept.int.perc = FALSE, accept.int.threshold = NULL,
        graph.int = c("CB", "PI"), include.int = FALSE, col.BLS = 1, col.CI = 2,
        col.CB = 3, col.PI = 4, col.GI = 5, lty.BLS = 1, lty.CI = 1,
        lty.CB = 1, lty.PI = 1, lty.GI = 1, ...)

Arguments

BLS.results

a BLS or BLS.ht class object (obtained with the BLS or BLS.ht functions).

xname

a character string for the name of the X device.

yname

a character string for the name of the Y device.

antilog

a character string or a numeric value. This argument display the BLS results on the initial scales on the (X,Y) plot if a logarithmic transformation was used prior to the BLS function. Options available are: 10 or "e".

accept.int

a numeric vector (length equal 1 or 2) for the value of : Y = X to assess whether two devices (X and Y) are equivalent or not. Two values of can be entered to take into account the case where the equivalence threshold changes along the X axis.

accept.int.perc

a logical variable (TRUE or FALSE) whether needs to be interpreted in percentage: Y = X %.

accept.int.threshold

a numeric value for the X threshold where the value of changes if two values are used in accept.int.

graph.int

a character vector for the hyperbolic curves to be displayed on the graph. Options are: "CI" (Confidence Intervals),"CB" (Confidence Bands),"PI" (Predictive Intervals),"GI" (Generalised Intervals).

include.int

a logical variable whether the hyperbolic intervals should lie entirely in the plot.

col.BLS

a character string or a numeric variable for the colour of the BLS regression line.

col.CI

a character string or a numeric variable for the colour of the confidence intervals.

col.CB

a character string or a numeric variable for the colour of the confidence bands.

col.PI

a character string or a numeric variable for the colour of the predictive intervals.

col.GI

a character string or a numeric variable for the colour of the generalized intervals.

lty.BLS

a numeric variable for the type of line of the BLS regression line.

lty.CI

a numeric variable for the type of line for the confidence intervals.

lty.CB

a numeric variable for the type of line for the confidence bands.

lty.PI

a numeric variable for the type of line for the predictive intervals.

lty.GI

a numeric variable for the type of line for the generalized intervals.

…

the common arguments from 'plot' or 'par' that may be used additionaly, such as xlim, ylim, xlab, ylab.

Value

An (X,Y) plot in a new window.

Details

The BLS.result argument is mandatory.

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

MD.plot

Examples

# NOT RUN {
library(BivRegBLS)
data(SBP)
# Estimate the BLS regression on replicated data
res.BLS=BLS(data=SBP,xcol=c("J1","J2","J3"),ycol=8:10,qx=3,qy=3)
# Plot the results in a (X,Y) plot with an acceptance interval
XY.plot(BLS.results=res.BLS,xname="J",yname="S",accept.int=10,accept.int.perc=FALSE)
XY.plot(BLS.results=res.BLS,xname="J",yname="S",accept.int=10,accept.int.perc=TRUE)
XY.plot(BLS.results=res.BLS,xname="J",yname="S",accept.int=c(10,20),
        accept.int.perc=FALSE,accept.int.threshold=150)
# }