glsControl               package:nlme               R Documentation

_C_o_n_t_r_o_l _V_a_l_u_e_s _f_o_r _g_l_s _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 'gls' function.

_U_s_a_g_e:

     glsControl(maxIter, msMaxIter, tolerance, msTol, msScale, msVerbose,
                singular.ok, qrTol, returnObject, apVar, .relStep,
                nlmStepMax, minAbsParApVar, natural)

_A_r_g_u_m_e_n_t_s:

 maxIter: maximum number of iterations for the 'gls' optimization
          algorithm. Default is 50.

msMaxIter: maximum number of iterations for the optimization step
          inside the 'gls' optimization. Default is 50.

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

   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 function). Default is
          'lmeScale'.

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

singular.ok: a logical value indicating whether non-estimable
          coefficients (resulting from linear dependencies among the
          columns of the regression matrix) should be allowed. Default
          is 'FALSE'.

   qrTol: a tolerance for detecting linear dependencies among the
          columns of the regression matrix in its QR decomposition.
          Default is '.Machine$single.eps'.

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'.

   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 calculations. Default
          is '.Machine$double.eps^(1/3)'.

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

minAbsParApVar: numeric value - minimum absolute parameter value in the
          approximate variance calculation.  The default is '0.05'.

 natural: logical.  Should the natural parameterization be used for the
          approximate variance calculations?  Default is 'TRUE'.

_V_a_l_u_e:

     a list with components for each of the possible arguments.

_A_u_t_h_o_r(_s):

     Jose Pinheiro Jose.Pinheiro@pharma.novartis.com and Douglas Bates
     bates@stat.wisc.edu

_S_e_e _A_l_s_o:

     'gls', 'lmeScale'

_E_x_a_m_p_l_e_s:

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

