SoloScattorPlot: Generate Scatter Plots for Time-to-Event and Biomarkers for...

Description Usage Arguments Author(s) References See Also Examples

View source: R/SoloScattorPlot.R

Description

This function will generate the scatter plot of time-to-event and biomarker for one dataset. It helps to visualize the relationship between survival endpoints and biomarkers.

Usage

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SoloScattorPlot(data, cutoff, xlab, ylab, main, ylim, xlim, col1, col2, lwd, pch1, pch2, 
legendloc, legendtxt, ncol)

Arguments

data

It is a data object with three variables included: event: the survival time, a positive numerical vector with no missing values; censor: the censor information, a vector with 1 indicating an event and 0 indicating right censored; marker: the biomarker information, or other interesting variables.

cutoff

This is to define the interesting data cutoff time point to see the relationship between time-to-events and markers.

xlab

It is the title for x axis; default is "Marker".

ylab

It is the title for y axis; default is "Time to Event".

main

It is the title for the plot; default is "Scattor Plot".

ylim

It creates the continuous scale of y axis of the plot; default is "c(0,3600)".

xlim

It creates the continuous scale of y axis of the plot; default is "c(0,100)".

col1

It defines the color of the dot; default is "red".

col2

It defines the color of the cutoff line; default is "red".

lwd

It defines the width of the cutoff line; default is "2".

pch1

It defines the type of the dot for event; default is "20".

pch2

It defines the type of the dot for censor; default is "21".

legendloc

It specifies the location of the legend; default is "bottomright".

legendtxt

It provides the text of the legend; default is "c("death","censor")".

ncol

It specifies the number of columns displayed in legend; default=1

Author(s)

Hui Yang huiy@amgen.com, Rui Tang rui_tang@vrtx.com and Jing Huang jinghuang0@gmail.com

References

Yang H., Tang R., Hale M. and Huang J. (2016) A visualization method measuring the performance of biomarkers for guiding treatment decisions Pharmaceutical Statistics, 15(2), 1539-1612

See Also

DuoScattorPlot, TrioScattorPlot

Examples

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	## Create the data object for the function

	o.data = data.frame(event=wpcdata$OSday, censor=wpcdata$OScensor, marker=wpcdata$Biomarker1)

	## Print out the figure:
	
	SoloScattorPlot(o.data,180,xlab=c("Marker"),ylab=c("Survival Rate"),
			main=c("Weighted Predictiveness Curve"),ylim=c(0,600),xlim=c(0,100),
			col1="red",col2="red",lwd=2,pch1=20,pch2=21,legendloc="bottomright",ncol=1)

WPC documentation built on May 2, 2019, 6:52 a.m.