YPmodel.estimate: YPmodel Estimate Parameters.

Description

The main function to fit the short-term and long-term hazard ration model.

Usage

# S3 method for estimate
YPmodel(data, startPoint, nm, maxIter1, maxIter2, interval, Internal, ...)
# S3 method for YPmodel.estimate
summary(object,...)
# S3 method for YPmodel.survivor
plot(x, Internal, ...)

Arguments

…

For S4 method only.

data

A properly qualified filename where text data is to be saved, or a dataframe of input data set with three vectors: the event / censoring time (unite: year), the censoring indicator, and the group membership indicator. See the structure of sample data set gastric for instance.

startPoint

Start point for estimating \(\hat{\beta}\).

The upper boundary for the absolute value of \(\hat{\beta}\), and the default value is \(\log(100)\).

maxIter1

Parameter of out-cycle iteration numbers.

maxIter2

Parameter of inner-cycle iteration numbers.

interval

A binary parameter to control whether or not to perform interval estimation of \(\hat{\beta}\), when it is set as 1, the interval estimation will be performed.

Internal

A dataframe of internal parameters, used only to perform hypothesis tests and plot (and to accelerate the speed).

A dataframe of estimation results, including estimation of \(\hat{\beta}\) and and its confidential intervals and \(\hat{R}(t,\hat{\beta})\), generated by YPmodel.estimate.

object

A dataframe of estimation results, including estimation of \(\hat{\beta}\) and and its confidential intervals and \(\hat{R}(t,\hat{\beta})\), generated by YPmodel.estimate, equally to x (different symbol for S4 method only).

Value

beta

Value of \(\hat{\beta}\).

Value of \(\hat{R}(t,\hat{\beta})\).

variance.beta1

Variance of the first variable of \(\hat{\beta}\).

variance.beta2

Variance of the second variable of \(\hat{\beta}\).

References

YANG, S. AND PRENTICE, R. L. (2005). Semiparametric analysis of short-term and long-term hazard ratios with two-sample survival data. Biometrika 92, 1-17.

Examples

Run this code

# NOT RUN {
    library(YPmodel)
    data(gastric)
    Estimate <- YPmodel.estimate(data=gastric, interval=1)

    Estimate <- YPmodel.estimate(data=gastric, startPoint=c(0,0), nm=log(100))

    Estimate <- YPmodel.estimate(data=gastric, maxIter1=50, maxIter2=20)

    summary(Estimate)

    plot(Estimate)
# }