lapadj {bqtl}  R Documentation 
lapadj provides the Laplace approximation to the marginal posterior (over coefficients and dispersion parameter) for a given genetical model for a quantitative trait. A byproduct is the parameter value corresponding to the maximum posterior or likelihood.
lapadj(reg.formula, ana.obj,
rparm = NULL, tol = 1e10,
return.hess = FALSE, mode.names = NULL, mode.mat = NULL,
maxit = 100, nem = 1,setup.only=FALSE,subset=NULL,casewt=NULL,
start.parm=NULL, ...)
reg.formula 
A formula, like

ana.obj 
See 
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
A vector with 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 
mode.mat 
Not usually set by the user. A matrix which indicates the values of regressor
variables corresponding to the allele states. If 
maxit 
Maximum Number of iterations to perform 
nem 
Number of EM iterations to use in reinitializing the pseudoHessian 
setup.only 
If TRUE, do not run. Return an object that can be use
for a direct call to 
subset 
expression to evaluate using 
casewt 
a vector of nonnegative weights 
start.parm 
Vector of starting values for the maximization 
... 
other objects needed in fitting 
The core of this function is a quasiNewton 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.
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. 
Charles C. Berry cberry@ucsd.edu
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.