Here is an example to calculate the tValues.

set.seed(2013)
# random generate simulation data with GPD distribution with 3 parameters
alpha<- 1; beta<- 2;

n<- 100;
err<- rnorm(n, sd=0.5)
x <-  rnorm(n)



# model y defined as
y<- alpha+beta*x + err

data<- data.frame(x, y)
write.csv(data, 'c:\\temp\\data.csv')


# define -2*loglikelihood function
LL<-function(params){
  alpha<- params[1]; beta<- params[2]; sigma<- params[3]
  
  linkfun<- alpha+beta*data$x
  
  error<-  data$y-linkfun
  
  ;
  
  f<- (1/(sigma*sqrt(2*pi))) * exp(-0.5*(error/sigma)**2)
  
  ll<- sum(log(f))
  return(-ll)
}

#optimized with initial condistion


modelResults<- optim(c(0,0,5), LL, method = "BFGS", hessian = TRUE)

std<- sqrt(diag(solve(modelResults$hessian)))
tValue<- modelResults$par/std
estiamtes<- cbind(modelResults$par, tValue)
estiamtes

x谢谢阿

谢谢，学习了！