如何正确优化负二项式？

Question

我在优化负二项式时遇到了一些问题。我总是得到同样的错误。我尝试使用积极的起始值，但这并没有解决问题

initial value in 'vmmin' is not finite

我做了什么：

1. 我定义了对数似然

   sum(lgamma((theta/(sigma2 - 1)) + 1) - lgamma(outcome+1) -lgamma(theta/(sigma2 -1))  + outcome*log((sigma2-1)/sigma2) -   (theta/(sigma2 - 1)) *log(sigma2))
   

2. 我将整个功能定义如下。这似乎运行：

   ll.negbin <- function(par, covariates, outcome){
   if(!all(covariates[,1] == 1))# here I add intercept{
   covariates <- as.matrix(cbind(1,covariates))}
   theta <- covariates%*%par[1:ncol(covariates)]
   gamma <- par[(ncol(covariates)+1)]
   sigma2 <- exp(gamma)+1
   out <- sum(lgamma((theta/(sigma2 - 1)) + 1) - lgamma(outcome+1) -lgamma(theta/(sigma2 -1)) + outcome*log((sigma2-1)/sigma2) -   (theta/(sigma2 - 1)) *log(sigma2))
   }
   

3. 使用optim优化：

   opt.negbin <- optim(par = rep(0, ncol(data[,2:6]) + 1),# here you can  put your in.var. inside
   fn = ll.negbin,
   covariates = data[,2:6], # here you can put your columns of in.var.(,2:6).
   outcome = data$,         # here you can put your dependent variable
   control = list(fnscale = -1),
   hessian = T,
   method = "BFGS")