clikcorr (version 1.0)

splot2: Graphical function 2 for visualizing bivariate censored and/or missing data.

Description

Generates scatter plot for bivariate data with different types of censoring and missing.

Usage

splot2(data, lower1, upper1, lower2, upper2, pch = 21, bg = "cyan", 
xlab = lower1, ylab = lower2, ...)

Arguments

data

a data frame name.

lower1

the lower bound name in the data frame of the first of the two variables for whose pairwise correlation to be calculated.

upper1

the upper bound name in the data frame of the first of the two variables for whose pairwise correlation to be calculated.

lower2

the lower bound name in the data frame of the second of the two variables for whose pairwise correlation to be calculated.

upper2

the upper bound name in the data frame of the second of the two variables for whose pairwise correlation to be calculated.

pch

point character.

bg

point background color.

xlab

x axis label.

ylab

y axis label.

...

not used.

Details

Generates scatter plot for bivariate data with different types of censoring and missing.

References

Yanming Li, Kerby Shedden, Brenda W. Gillespie and John A. Gillespie (2016). Calculating Profile Likelihood Estimates of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data.

Examples

data(ND)
logND <- log(ND)

splot2(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678",
 "t2_HxCDF_234678", xlab="OCDD", ylab="HxCDF234678")

x <- logND[which(!is.na(logND[,14]) & !is.na(logND[,15])),14]
y <- logND[which(!is.na(logND[,26]) & !is.na(logND[,27])),26]
xhist = hist(x, plot=FALSE, breaks=10)
yhist = hist(y, plot=FALSE, breaks=10)
  
zones=matrix(c(2,0,1,3), ncol=2, byrow=TRUE)
layout(zones, widths=c(5/6,1/6), heights=c(1/6,5/6))
top = max(c(xhist$counts, yhist$counts))
par(mar=c(5,5,1,1))
splot2(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678",
 "t2_HxCDF_234678", xlab="OCDD", ylab="HxCDF234678", cex=1.5)  

par(mar=c(0,6,2,4))
barplot(xhist$counts, axes=FALSE, ylim=c(0, max(xhist$counts)), space=0)
par(mar=c(6,0,4,2))
barplot(yhist$counts, axes=FALSE, xlim=c(0, max(yhist$counts)), space=0, horiz=TRUE)