Question

我正在尝试获得似然比检验的功效，以便将伽马分布与广义伽马分布进行比较。因此，我需要使用三个参数对广义伽玛分布进行最大似然估计。我编写的代码如下。

library(bbmle)
library(flexsurv)

sig=0.05
den=1000

n=30
apar=2 ###alfa
bpar=3 ##beta
cpar=4 ##c parametresi

LRatio=function(den,n,par=c(cpar,apar,bpar)){

  LR2=rep(0,den)

  count=rep(0,den)

  cpar=par[1]
  apar=par[2]
  bpar=par[3]

  for(i in 1:den){

    y=rgengamma.orig(n,shape=cpar,scale=bpar,k=apar)

    gamma4 = function(shape, scale) {
      -sum(dgamma(y, shape = shape, scale = scale,log = TRUE))
    }

    gm = mean(y)
    cv = var(y)/mean(y)


    m5 = mle2(gamma4, start = list(shape = gm/cv, scale = cv),method = "L-BFGS-B", lower =c(.00001,.00001),upper = c(Inf,Inf))

    gengamma3 = function(shape, scale,k) {
      -sum(dgengamma.orig(y, shape = shape, scale = scale,k=k,log =TRUE))
      #-(n*log(abs(shape))-n*shape*k*log(scale)+(shape*k-1)*sum(log(y))-n*log(exp(lgamma(k)))-sum((y/scale)^shape))
    }


    ci=mean(y)            #c initial value
    a1=ci*mean(y)^(ci-1)
    a2=ci*(ci-1)*(mean(y)^(ci-1))/2
    mu1=mean(y)^ci+a2*mean(y^2)
    mu2=(a1^2)*mean(y^2)+2*a1*a2*mean(y^3)+(a2^2)*(mean(y^4)-mean(y^2)^2)
    alp =(mu1^2)/mu2                            #alpha initial value
    bet=mean(y)*gamma(alp)/gamma(alp+(1/ci))       #beta initial value


    m6 = mle2(gengamma3,start = list(shape = ci, scale = bet, k=alp),method = "L-BFGS-B", lower = c(0, 0,0),upper = c(Inf, Inf, Inf))

    LR2[i]=2*(logLik(m6)-logLik(m5))
    count[i]=LR2[i]>=qchisq(1-sig, df=1) 

  }

  pow=sum(count)/den
  print(i)
  print(pow)
}

但我收到了错误：

optim(par = c(3.88907163215354, 3.62005456122935, 1.66499331462506 :
  L-BFGS-B needs finite values of 'fn'

有什么问题？

mle2功能：L-BFGS-B需要有限的“fn”和“fn”。

0 个答案: