Question

我尝试了以下代码来估算以下参数：alpha，beta，k，lambda＆amp; p我建议的发行版。 r代码如下所示。

library('bbmle')

xvec <- c(0.55, 0.93, 1.25, 1.36, 1.49, 1.52, 1.58, 1.61, 1.64, 1.68, 1.73, 
1.81, 2,0.74, 1.04, 1.27, 1.39, 1.49, 1.53, 1.59, 1.61, 1.66, 1.68, 1.76, 
1.82,2.01, 0.77, 1.11, 1.28, 1.42, 1.5, 1.54, 1.6, 1.62, 1.66, 1.69, 1.76, 
1.84, 2.24, 0.81, 1.13, 1.29, 1.48, 1.5, 1.55, 1.61, 1.62, 1.66, 1.7, 1.77,
1.84, 0.84, 1.24, 1.3, 1.48, 1.51, 1.55, 1.61, 1.63, 1.67, 1.7, 1.78, 1.89)

n <- length(xvec)

ln <- function(alpha, beta, k, lambda, p) {
  -sum(log(alpha*beta*xvec**(alpha-1) + k*lambda*xvec**(k-1)*(1+xvec**k)**(-1))) + sum(lambda*log(1+xvec**k))-(n*log(1-p)) + beta*sum(xvec**alpha)-2*sum(log(1-p*(exp(-beta*xvec**alpha)/(1+xvec**k)**lambda)))
}

mle2(minuslogl = ln, start = list(alpha = 1, beta = 15, k = 1, 
                                  lambda = 0.5, p = 0.4), hessian.opts = TRUE)

summary(mle.result1)

我对参数使用了不同的初始值而没有很好的估计（上面的代码中显示了最后一组）。获得的最后一个解决方案是：

Maximum likelihood estimation

Call:

mle2(minuslogl = ln, start = list(alpha = 1, beta = 15, k = 1, 
lambda = 0.5, p = 0.4), hessian.opts = TRUE)

Coefficients:
       Estimate    Std.Error   z value  Pr(z)
alpha   0.15002         NA       NA     NA
beta   15.10368         NA       NA     NA
k       0.98189         NA       NA     NA
lambda  0.58166         NA       NA     NA
p      -0.05312    67.82338   -8e-04 0.9994

-2 log L: -1.229098e-06 

Warning message:
In sqrt(diag(object@vcov)) : NaNs produced

拜托，我哪里出错了？谢谢。

我哪里做错了？

0 个答案: