clikcorr (version 1.0)

splot: Graphical function for visualizing bivariate censored and/or missing data

Description

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

Usage

splot(data, lower.list, upper.list, ti =ifelse(length(lower.list)>2, 
paste("Scatter plots of", lower.list[1], "to", lower.list[length(lower.list)]), 
paste("Scatter plot of", lower.list[1], "and", lower.list[2])), legend = TRUE, cex = 1.5, ...)

Arguments

data

a data frame name.

lower.list

the lower bounds names in the data frame of the variables between which the scatter plots are to be generated.

upper.list

the upper bounds names in the data frame of the variables between which the scatter plots are to be generated.

ti

figure title.

legend

figure legend.

cex

simbol sizes.

...

not used.

Details

Generates matrix of scatter plots 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)

splot(logND, c("t1_OCDD", "t1_TCDF", "t1_HxCDF_234678"),
 c("t2_OCDD", "t2_TCDF", "t2_HxCDF_234678"), ti="scatter plot matrix")

splot(logND, c("t1_OCDD", "t1_TCDF", "t1_HxCDF_234678"),
 c("t2_OCDD", "t2_TCDF", "t2_HxCDF_234678"), ti="scatter plot matrix", bg="gold")