nlm                  package:stats                  R Documentation

_N_o_n-_L_i_n_e_a_r _M_i_n_i_m_i_z_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This function carries out a minimization of the function 'f' using
     a Newton-type algorithm.  See the references for details.

_U_s_a_g_e:

     nlm(f, p, hessian = FALSE, typsize=rep(1, length(p)), fscale=1,
         print.level = 0, ndigit=12, gradtol = 1e-6,
         stepmax = max(1000 * sqrt(sum((p/typsize)^2)), 1000),
         steptol = 1e-6, iterlim = 100, check.analyticals = TRUE, ...)

_A_r_g_u_m_e_n_t_s:

       f: the function to be minimized.  If the function value has an
          attribute called 'gradient' or both 'gradient' and 'hessian'
          attributes, these will be used in the calculation of updated
          parameter values.  Otherwise, numerical derivatives are used.
          'deriv' returns a function with suitable 'gradient'
          attribute.  This should be a function of a vector of the
          length of 'p' followed by any other arguments specified by
          the '...' argument.

       p: starting parameter values for the minimization.

 hessian: if 'TRUE', the hessian of 'f' at the minimum is returned.

 typsize: an estimate of the size of each parameter at the minimum.

  fscale: an estimate of the size of 'f' at the minimum.

print.level: this argument determines the level of printing which is
          done during the minimization process.  The default value of
          '0' means that no printing occurs, a value of '1' means that
          initial and final details are printed and a value of 2 means
          that full tracing information is printed.

  ndigit: the number of significant digits in the function 'f'.

 gradtol: a positive scalar giving the tolerance at which the scaled
          gradient is considered close enough to zero to terminate the
          algorithm.  The scaled gradient is a measure of the relative
          change in 'f' in each direction 'p[i]' divided by the
          relative change in 'p[i]'.

 stepmax: a positive scalar which gives the maximum allowable scaled
          step length.  'stepmax' is used to prevent steps which would
          cause the optimization function to overflow, to prevent the
          algorithm from leaving the area of interest in parameter
          space, or to detect divergence in the algorithm. 'stepmax'
          would be chosen small enough to prevent the first two of
          these occurrences, but should be larger than any anticipated
          reasonable step.

 steptol: A positive scalar providing the minimum allowable relative
          step length.

 iterlim: a positive integer specifying the maximum number of
          iterations to be performed before the program is terminated.

check.analyticals: a logical scalar specifying whether the analytic
          gradients and Hessians, if they are supplied, should be
          checked against numerical derivatives at the initial
          parameter values. This can help detect incorrectly formulated
          gradients or Hessians.

     ...: additional arguments to 'f'.

_D_e_t_a_i_l_s:

     If a gradient or hessian is supplied but evaluates to the wrong
     mode or length, it will be ignored if 'check.analyticals = TRUE'
     (the default) with a warning.  The hessian is not even checked
     unless the gradient is present and passes the sanity checks.

     From the three methods available in the original source, we always
     use method "1" which is line search.

_V_a_l_u_e:

     A list containing the following components: 

 minimum: the value of the estimated minimum of 'f'.

estimate: the point at which the minimum value of 'f' is obtained.

gradient: the gradient at the estimated minimum of 'f'.

 hessian: the hessian at the estimated minimum of 'f' (if requested).

    code: an integer indicating why the optimization process
          terminated.

          _1: relative gradient is close to zero, current iterate is
               probably solution.

          _2: successive iterates within tolerance, current iterate is
               probably solution.

          _3: last global step failed to locate a point lower than
               'estimate'.  Either 'estimate' is an approximate local
               minimum of the function or 'steptol' is too small.

          _4: iteration limit exceeded.

          _5: maximum step size 'stepmax' exceeded five consecutive
               times.  Either the function is unbounded below, becomes
               asymptotic to a finite value from above in some
               direction or 'stepmax' is too small.

iterations: the number of iterations performed.

_R_e_f_e_r_e_n_c_e_s:

     Dennis, J. E. and Schnabel, R. B. (1983) _Numerical Methods for
     Unconstrained Optimization and Nonlinear Equations._
     Prentice-Hall, Englewood Cliffs, NJ.

     Schnabel, R. B., Koontz, J. E. and Weiss, B. E. (1985) A modular
     system of algorithms for unconstrained minimization. _ACM Trans.
     Math. Software_, *11*, 419-440.

_S_e_e _A_l_s_o:

     'optim' and 'nlminb'.

     'constrOptim' for constrained optimization,  'optimize' for
     one-dimensional minimization and 'uniroot' for root finding.
     'deriv' to calculate analytical derivatives.

     For nonlinear regression, 'nls' may be better.

_E_x_a_m_p_l_e_s:

     f <- function(x) sum((x-1:length(x))^2)
     nlm(f, c(10,10))
     nlm(f, c(10,10), print.level = 2)
     str(nlm(f, c(5), hessian = TRUE))

     f <- function(x, a) sum((x-a)^2)
     nlm(f, c(10,10), a=c(3,5))
     f <- function(x, a)
     {
         res <- sum((x-a)^2)
         attr(res, "gradient") <- 2*(x-a)
         res
     }
     nlm(f, c(10,10), a=c(3,5))

     ## more examples, including the use of derivatives.
     ## Not run: demo(nlm)

