

nlmeControl(nlme)                            R Documentation

_C_o_n_t_r_o_l _V_a_l_u_e_s _f_o_r _n_l_m_e _F_i_t

_D_e_s_c_r_i_p_t_i_o_n_:

     The values supplied in the function call replace the
     defaults and a list with all possible arguments is
     returned. The returned list is used as the `control'
     argument to the `nlme' function.

_U_s_a_g_e_:

     nlmeControl(maxIter, pnlsMaxIter, msMaxIter, minScale, tolerance,
                 niterEM, pnlsTol, msTol, msScale, returnObject, msVerbose,
                 gradHess, apVar, nlmStepMax, .relStep, natural)

_A_r_g_u_m_e_n_t_s_:

 maxIter: maximum number of iterations for the `nlme' opti-
          mization algorithm. Default is 50.

pnlsMaxIter: maximum number of iterations for the `PNLS'
          optimization step inside the `nlme' optimization.
          Default is 7.

msMaxIter: maximum number of iterations for the `ms' opti-
          mization step inside the `nlme' optimization.
          Default is 50.

minScale: minimum factor by which to shrink the default step
          size in an attempt to decrease the sum of squares
          in the `PNLS' step.  Default 0.001.

tolerance: tolerance for the convergence criterion in the
          `nlme' algorithm. Default is 1e-6.

 niterEM: number of iterations for the EM algorithm used to
          refine the initial estimates of the random effects
          variance-covariance coefficients. Default is 25.

 pnlsTol: tolerance for the convergence criterion in `PNLS'
          step. Default is 1e-3.

   msTol: tolerance for the convergence criterion in `ms',
          passed as the `rel.tolerance' argument to the
          function (see documentation on `ms'). Default is
          1e-7.

 msScale: scale function passed as the `scale' argument to
          the `ms' function (see documentation on that func-
          tion). Default is `lmeScale'.

returnObject: a logical value indicating whether the fitted
          object should be returned when the maximum number
          of iterations is reached without convergence of
          the algorithm. Default is `FALSE'.

msVerbose: a logical value passed as the `trace' argument to
          `ms' (see documentation on that function). Default
          is `FALSE'.

gradHess: a logical value indicating whether numerical gra-
          dient vectors and Hessian matrices of the log-
          likelihood function should be used in the `ms'
          optimization. This option is only available when
          the correlation structure (`corStruct') and the
          variance function structure (`varFunc') have no
          "varying" parameters and the `pdMat' classes used
          in the random effects structure are `pdSymm' (gen-
          eral positive-definite), `pdDiag' (diagonal),
          `pdIdent' (multiple of the identity),  or `pdComp-
          Symm' (compound symmetry). Default is `TRUE'.

   apVar: a logical value indicating whether the approximate
          covariance matrix of the variance-covariance
          parameters should be calculated. Default is
          `TRUE'.

.relStep: relative step for numerical derivatives calcula-
          tions. Default is `.Machine$double.eps^(1/3)'.

nlmStepMax: stepmax value to be passed to nlm. See `nlm' for
          details. Default is 100.0

 natural: a logical value indicating whether the `pdNatural'
          parametrization should be used for general posi-
          tive-definite matrices (`pdSymm') in `reStruct',
          when the approximate covariance matrix of the
          estimators is calculated. Default is `TRUE'.

_V_a_l_u_e_:

     a list with components for each of the possible argu-
     ments.

_A_u_t_h_o_r_(_s_)_:

     Jose Pinheiro and Douglas Bates

_S_e_e _A_l_s_o_:

     `nlme', `ms', `nlmeStruct'

_E_x_a_m_p_l_e_s_:

     # decrease the maximum number iterations in the ms call and
     # request that information on the evolution of the ms iterations be printed
     nlmeControl(msMaxIter = 20, msVerbose = TRUE)

