aft.kmweight: Computing Kaplan-Meier Weights

In imputeYn: Imputing the Last Largest Censored Observation(s) Under Weighted Least Squares

Description

Compute Kaplan-Meier weights for weighted least squares method.

Usage

aft.kmweight(Y, delta)

Arguments

Y	survival time.
delta	status.

Details

Compute Kaplan-Meier weights that are used for weighted least squares to solve the AFT model under right censoring. This gives weights that are computed after implementation of Efron's (1967) tail correction.

Value

The Kaplan-Meier weights are proper in the sense that they sum one. The censoring considered here is right censoring only.

kmwt	The Kaplan Meier weights

Author(s)

Hasinur Rahaman Khan and Ewart Shaw

References

Stute, W. (1993). Consistent estimation under random censorship when covariables are available. Journal of Multivariate Analysis, 45 , 89-103.

Efron, B. (1967). The two sample problem with censored data. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 4, p. 831-853.

Examples

# For dataset where the last largest datum is censored and censoring level is 50 percent
data1<-data(n=100, p=2, r=0, b1=c(2,4), sig=1, Cper=0)
kmw<-aft.kmweight(data1$y,data1$delta)
kmw

Example output

sh: 1: cannot create /dev/null: Permission denied
Loading required package: quadprog
Loading required package: emplik
Loading required package: mvtnorm
Loading required package: survival
Loading required package: boot

Attaching package: ‘boot’

The following object is masked from ‘package:survival’:

    aml


Attaching package: ‘imputeYn’

The following object is masked from ‘package:utils’:

    data

$kmwts
  [1] 0.01000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
  [7] 0.00000000 0.00000000 0.01076087 0.00000000 0.00000000 0.00000000
 [13] 0.00000000 0.00000000 0.01138650 0.00000000 0.00000000 0.00000000
 [19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
 [25] 0.01273490 0.00000000 0.00000000 0.00000000 0.01326552 0.00000000
 [31] 0.00000000 0.01365003 0.00000000 0.00000000 0.01406367 0.00000000
 [37] 0.00000000 0.01451013 0.00000000 0.00000000 0.00000000 0.01524794
 [43] 0.00000000 0.00000000 0.01579251 0.00000000 0.01608496 0.01608496
 [49] 0.01608496 0.00000000 0.01640666 0.00000000 0.01674847 0.00000000
 [55] 0.01711256 0.00000000 0.01750149 0.01750149 0.00000000 0.01792835
 [61] 0.01792835 0.01792835 0.00000000 0.00000000 0.01892437 0.01892437
 [67] 0.01892437 0.01892437 0.01892437 0.00000000 0.01955518 0.01955518
 [73] 0.01955518 0.01955518 0.00000000 0.02033739 0.02033739 0.02033739
 [79] 0.02033739 0.02033739 0.02033739 0.02033739 0.02033739 0.02033739
 [85] 0.00000000 0.02169322 0.00000000 0.00000000 0.02530875 0.02530875
 [91] 0.02530875 0.02530875 0.00000000 0.02892429 0.02892429 0.02892429
 [97] 0.02892429 0.02892429 0.02892429 0.02892429

imputeYn documentation built on May 1, 2019, 10:52 p.m.