plot.multidf {survivalREC}

Plot methods for a multidf object

Description

Provides the plots for the bivariate distribution function and marginal distribution of the second time.

Usage

## S3 method for class 'multidf'
plot(x, t1, method = "KMW", type = "s", ...)

Arguments

x

An object of class multidf.

t1

Value of the first gap time.

method

A character string specifying which estimator to fit. Possible values are "KMW", "LIN", "WCH" and "LANDMARK".

type

The type of plot that should be drawn. See details par for possible options. Defaults to "s" for the draw be stair steps.

...

Other options.

Value

No value is returned.

Author(s)

Gustavo Soutinho and Luis Meira-Machado

References

de Una-Alvarez, J. and Meira-Machado, L. (2008). A simple estimator of the bivariate distribution function for censored gap times, Statistics and Probability Letters 78, 2440-2445.

Davison, A.C. and Hinkley, D.V. (1997) "Bootstrap Methods and Their Application", Chapter 5. Cambridge University Press.

van Houwelingen, H.C. (2007). Dynamic prediction by landmarking in event history analysis, Scandinavian Journal of Statistics, 34, 70-85. Kaplan, E. and Meier, P. (1958). Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association 53(282), 457-481.

See Also

KMWdf, LDMdf, LINdf and WCHdf.

Examples

data("bladder4state")

b3state<-multidf(gap1=bladder4state$y1, event1=bladder4state$d1, 
                 gap2=bladder4state$y2, status=bladder4state$d2, 
                 size=bladder4state$size)

head(b3state[[1]])

KMWdf(b3state,x=13,y=20)
LDMdf(b3state,x=13,y=20)
LINdf(b3state,x=13,y=20)
WCHdf(b3state,x=13,y=20)

plot(x=b3state, t1=3, method="KMW", type = "s")
plot(x=b3state, t1=3, method="LIN", type = "s")
plot(x=b3state, t1=3, method="WCH", type = "s")
plot(x=b3state, t1=3, method="LANDMARK", type = "s")