How to properly optimize negative binomial?

I have some problems optimizing negative binomial. I always get the same error. I tried to play around with positive initial values, but that doesn't solve the problem.

initial value in 'vmmin' is not finite

      

        
        
        
      

    

What I've done:

• I have determined the log likelihood

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

• I have defined the whole function as follows. This seems to work:

  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))
  }
  
        
  
          
          
          
        
  
      

• optimized with optimization:

  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")