Description Usage Arguments Details Value Author(s) References Examples
Maximum rank-based likelihood estimates of the proportionality parameter θ and probability masses of the discretized baseline distribution F.
1 2 |
x, y |
Vectors containing the data values of the two samples x1,...,xm and y1,...,yn. |
theta |
Initial value for proportionality parameter θ. |
p |
Initial value for probability masses p1,...,pN of the discretized baseline distribution F. |
tol |
Convergence tolerance used in the Newton iteration |
maxit |
The maximum number of Newton iterations. |
The Newton iteration method is applied to find the maximum rank-based
likelihood estimates of the proportionality parameter
θ and probability masses p1,...,pN of the discretized
baseline distribution F. If the default initial values for theta
and/or
p
do not work, functions newton.theta
,
dd.est
and phi
can be used to calculate other initials.
theta |
The maximum rank-based likelihood estimate of the proportionality parameter theta. |
p |
The maximum rank-based likelihood estimate of probability masses p1,...,pN of the discretized baseline distribution F. |
ell |
The maximum rank-based loglikelihood. |
del |
Convergent tolerance which is sum of the absolute scores, and absolute changes of the
parameters |
Zhong Guan <zguan@iusb.edu>
Zhong Guan and Cheng Peng (2011), "A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness-of-fit", Journal of Nonparametric Statistics, to appear.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Use radar tube life data
z<-RadarTube$Days
v<-RadarTube$Type
x<-z[v==1]; y<-z[v==2]
# Dabrowska-Doksum's estimate of theta
theta0.hat<-dd.est(x,y)
theta0.hat
vartheta0.hat<-dd.est(y,x)
vartheta0.hat
## mrle
m<-length(x)
n<-length(y)
N<-m+n
lambda<-m/N
phat0<-phi(N, theta0.hat, lambda)/N
mrle.sporm(x, y, theta0.hat, phat0)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.