WRegEst | R Documentation |
For the AFT model, this function computes the case weighted estimator of beta. Either the least squares estimator or the regression quantile estimator.
WRegEst(x, y, delta, LS=TRUE, tau=0.5)
x |
a matrix of size N by q. |
y |
a vector of length N, containing the censored responses. Usually the log of the original observed failure times. |
delta |
a vector (length N) of either 1's or 0's. d=1 means y is uncensored; d=0 means y is right censored. |
LS |
a logical value. If TRUE then the function will
return the least squares estimator. If FALSE then the
function will return the quantile regression estimator,
with the quantile level specified by |
tau |
a scalar, between 0 and 1. The quantile to be used in quantile regression. If tau=0.5 then it is the median regression. If LS=TRUE, then it is ignored. |
Due to the readily available minimizer, we only provide least squares
and quantile regression here. However, in the companion testing function
WRegTest
the user can supply a self defined psifun
function,
corresponding to the general M-estimation in the regression modeling.
(since there is no minimization needed).
The estimator is the minimizer of
\sum_{i=1}^n w_i \rho (Y_i - X_i b)
Assuming a correlation model
Y_i = X_i \beta + \sigma(X_i) \epsilon_i
where \rho( )
is either the square or the absolute value function.
The estimator \hat \beta
.
Mai Zhou.
Zhou, M.; Bathke, A. and Kim, M. (2012). Empirical likelihood analysis of the Heteroscastic Accelerated Failure Time model. Statistica Sinica, 22, 295-316.
data(smallcell)
WRegEst(x=cbind(1,smallcell[,1],smallcell[,2]),
y=smallcell[,3], delta=smallcell[,4])
####################################################
#### you should get x1 x2 x3
#### -59.22126 -488.41306 16.03259
####################################################
WRegEst(x=cbind(1,smallcell[,1],smallcell[,2]),
y=log10(smallcell[,3]), delta=smallcell[,4], LS=FALSE)
########################################################
#### you should get
#### [1] 2.603342985 -0.263000044 0.003836832
########################################################
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