lapadj                 package:bqtl                 R Documentation

_A_p_p_r_o_x_i_m_a_t_e _m_a_r_g_i_n_a_l _p_o_s_t_e_r_i_o_r _f_o_r _c_h_o_s_e_n _m_o_d_e_l

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

     lapadj provides the Laplace approximation to the marginal
     posterior (over coefficients and dispersion parameter) for a given
     genetical model for a quantitative trait. A by-product is the
     parameter value corresponding to the maximum posterior or
     likelihood.

_U_s_a_g_e:

      lapadj(reg.formula, ana.obj,  
              rparm = NULL,  tol = 1e-10,
             return.hess = FALSE, mode.names = NULL, mode.mat = NULL,
               maxit = 100, nem = 1,setup.only=FALSE,subset=NULL,casewt=NULL,
              start.parm=NULL, ...)

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

reg.formula: A formula, like 'y~add.X.3+dom.X.3+add.x.45*add.x.72'

 ana.obj: See'make.analysis.obj', which returns objects like this

   rparm: One of the following:

          A scalar that will be used as the ridge parameter for all
          design terms except for the intercept ridge parameter which
          is set to zero 

          A vector who named elements  can be matched by the design
          term names returned in '$reg.vec'. If no term named
          "intercept" is provided,

          'rparm["intercept"]' will be set to zero. 

          A vector with '(q-1)*k' elements (this works when there are
          no interactions specified). If names are provided, these will
          be used for matching.

          Positive entries are 'ridge' parameters or variance ratios in
          a Bayesian prior for the regression coefficients. Larger
          values imply more shrinkage or a more concentrated prior for
          the regresion coefficients.    

     tol: Iteration control parameter  

return.hess: Logical, include the Hessian in the output? 

mode.names: names to use as 'dimnames(mode.mat)[[2]]'  

mode.mat: Not usually set by the user. A matrix which indicates the
          values of regressor variables corresponding to the allele
          states. If 'mode.mat' is not given by the user,
          'ana.obj$mode.mat' is used.

   maxit: Maximum Number of iterations to perform

     nem: Number of EM iterations to use in reinitializing the
          pseudo-Hessian 

setup.only: If TRUE, do not run. Return an object that can be use for a
          direct call to '.C'

  subset: expression to evaluate using 'ana.obj$data' as the
          environment

  casewt : a vector of non-negative weights 

start.parm: Vector of starting values for the maximization

     ...: other objects needed in fitting 

_D_e_t_a_i_l_s:

     The core of this function is a quasi-Newton optimizer due to
     Minami (1993) that has a computational burden that is only a bit
     more than the EM algorithm, but features fast convergence.  This
     is used to find the mode of the posterior. Once this is in hand,
     one can find the Laplace approximation to the marginal likelihood.
     In addition, some useful quantities are provided that help in
     estimating the marginal posterior over groups of models.

_V_a_l_u_e:

     A list with components to be used in constructing approximations
     to the marginal posterior or a list that can be used to call the
     underlying C code directly. In the former case, these are: 

     adj: The ratio of the laplace approximation to the posterior for
          the correct likelihood to the laplace approximation to the
          posterior for  the linearized likelihood

 logpost: The logarithm of the posterior or likelihood at the mode

    parm: the location of the mode

posterior: The laplace approximation of the marginal posterior for the
          exact likelihood

hk.approx: Laplace approximation to the linearized likelihood

hk.exact: Exact marginal posterior for the linearized likelihood

 reg.vec: A vector of the variables used

   rparm: Values of ridge parameters used in this problem.

_A_u_t_h_o_r(_s):

     Charles C. Berry cberry@ucsd.edu

_R_e_f_e_r_e_n_c_e_s:

     Berry C.C.(1998) Computationally Efficient Bayesian QTL Mapping in
     Experimental Crosses. _ASA Proceedings of the Biometrics Section_.
     164-169.

     Minami M. (1993) Variance estimation for simultaneous response
     growth curve models.  Thesis (Ph. D.)-University of California,
     San Diego, Department of Mathematics.

