BJnoint {emplik} | R Documentation |
Compute the Buckley-James estimator in the regression model
y_i = \beta x_i + \epsilon_i
with right censored y_i
. Iteration method.
BJnoint(x, y, delta, beta0 = NA, maxiter=30, error = 0.00001)
x |
a matrix or vector containing the covariate, one row per observation. |
y |
a numeric vector of length N, censored responses. |
delta |
a vector of length N, delta=0/1 for censored/uncensored. |
beta0 |
an optional vector for starting value of iteration. |
maxiter |
an optional integer to control iterations. |
error |
an optional positive value to control iterations. |
This function compute the Buckley-James estimator
when your model do not have an intercept term.
Of course, if you include a column of 1's in the x matrix,
it is also OK with this function and it
is equivalent to having an intercept term.
If your model do have an intercept term, then you could also (probably should) use the function
bj( )
in the Design
library. It should be more refined
than BJnoint
in the stopping rule for the iterations.
This function is included here mainly to produce the estimator value
that may provide some useful information with the function bjtest( )
.
For example you may want to test a beta value near the
Buckley-James estimator.
A list with the following components:
beta |
the Buckley-James estimator. |
iteration |
number of iterations performed. |
Mai Zhou.
Buckley, J. and James, I. (1979). Linear regression with censored data. Biometrika, 66 429-36.
x <- matrix(c(rnorm(50,mean=1), rnorm(50,mean=2)), ncol=2,nrow=50)
## Suppose now we wish to test Ho: 2mu(1)-mu(2)=0, then
y <- 2*x[,1]-x[,2]
xx <- c(28,-44,29,30,26,27,22,23,33,16,24,29,24,40,21,31,34,-2,25,19)