mle                  package:stats4                  R Documentation

_M_a_x_i_m_u_m _L_i_k_e_l_i_h_o_o_d _E_s_t_i_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Estimate parameters by the method of maximum likelihood.

_U_s_a_g_e:

     mle(minuslogl, start = formals(minuslogl), method = "BFGS",
         fixed = list(), ...)

_A_r_g_u_m_e_n_t_s:

minuslogl: Function to calculate negative log-likelihood.

   start: Named list. Initial values for optimizer.

  method: Optimization method to use. See 'optim'.

   fixed: Named list.  Parameter values to keep fixed during
          optimization.

     ...: Further arguments to pass to 'optim'.

_D_e_t_a_i_l_s:

     The 'optim' optimizer is used to find the minimum of the negative
     log-likelihood.  An approximate covariance matrix for the
     parameters is obtained by inverting the Hessian matrix at the
     optimum.

_V_a_l_u_e:

     An object of class 'mle-class'.

_N_o_t_e:

     Be careful to note that the argument is -log L (not -2 log L). It
     is for the user to ensure that the likelihood is correct, and that
     asymptotic likelihood inference is valid.

_S_e_e _A_l_s_o:

     'mle-class'

_E_x_a_m_p_l_e_s:

     x <- 0:10
     y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
     ll <- function(ymax=15, xhalf=6)
         -sum(stats::dpois(y, lambda=ymax/(1+x/xhalf), log=TRUE))
     (fit <- mle(ll))
     mle(ll, fixed=list(xhalf=6))

     summary(fit)
     logLik(fit)
     vcov(fit)
     plot(profile(fit), absVal=FALSE)
     confint(fit)

     ## use bounded optimization
     ## the lower bounds are really > 0, but we use >=0 to stress-test profiling
     (fit1 <- mle(ll, method="L-BFGS-B", lower=c(0, 0)))
     plot(profile(fit1), absVal=FALSE)

     ## a better parametrization:
     ll2 <- function(lymax=log(15), lxhalf=log(6))
         -sum(stats::dpois(y, lambda=exp(lymax)/(1+x/exp(lxhalf)), log=TRUE))
     (fit2 <- mle(ll2))
     plot(profile(fit2), absVal=FALSE)
     exp(confint(fit2))

