# R/Compare_plot.R In MethodCompare: Bias and Precision Plots to Compare Two Measurements with Possibly Heteroscedastic Measurement Errors

#### Documented in compare_plot

```#' Plot used to visualize the recalibration of the new method after estimating the bias
#'
#' This function allows the visualization of the bias-corrected values (i.e.
#' recalibrated values, variable y1_corr) of the new measurement method.
#'
#' @param object an object retunred by a call to \link{measure_compare}
#' @author  Mingkai Peng
#' @export
#' @examples
#' ### load the data
#' data(data1)
#' ### analysis
#' measure_model <- measure_compare(data1)
#' ### compare plot
#' compare_plot(measure_model)
#'

compare_plot <- function(object){
data_old <- object\$Ref
data_new <- object\$New
Models <- object\$Models
max <- max(data_old\$y2,data_new\$y1,data_new\$y1_corrected)
min <- min(data_old\$y2,data_new\$y1,data_new\$y1_corrected)
range <- max-min
par(mar=c(3.5,3.5,2,2)+0.1)
plot(data_old\$y2_hat,data_old\$y2,pch=1,cex=0.5,col="grey",axes = F,
xlab="",ylab="",ylim=c(min-range*0.1,max+range*0.2))
title(main="Comparison of the methods",cex.main=0.9)
xlab="Average:(y1+y2)/2"
### Add the y axis
axis(2,col="black",las=1)
mtext("Measurement method",side = 2,line=2)
box(col="black")
### Add the x axis
axis(1)
mtext("BLUP of x",side=1,col="black",line=2)

points(data_new\$y2_hat,data_new\$y1,pch=19,col="blue",cex=0.5)
points(data_new\$y2_hat,data_new\$y1_corrected,pch=18,col="red",cex=0.5)
abline(Models[[2]]\$coefficients,lwd=2)
abline(Models[[4]]\$coefficients,lwd=2,lty=1,col="blue")
abline(Models[[6]]\$coefficients,lwd=2,lty=2,col="red")
legend("topleft",legend=c("Reference method (y2)","New method (y1)",
"New method(corrected)"),
pch=c(1,19,18),lty = c(1,1,2),col=c("black","blue","red"),
y.intersp = 0.7,yjust=0.2,bty = "n",cex=0.8)
}
```

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MethodCompare documentation built on May 30, 2017, 7:20 a.m.