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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.
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 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:
The ratio of the laplace approximation to the posterior for the correct likelihood to the laplace approximation to the posterior for the linearized likelihood
The logarithm of the posterior or likelihood at the mode
the location of the mode
The laplace approximation of the marginal posterior for the exact likelihood
Laplace approximation to the linearized likelihood
Exact marginal posterior for the linearized likelihood
A vector of the variables used
Values of ridge parameters used in this problem.
A formula, like
y~add.X.3+dom.X.3+add.x.45*add.x.72
Seemake.analysis.obj, which returns
objects like this
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 $reg.vec. If no term named
"intercept" is provided,
rparm["intercept"] will be set to zero.
A vector with (q-1)*k elements (this works when there are
no interactions specified). If names are provided, these will be
used for matching.
Positive entries are 'ridge' parameters or variance ratios in a Bayesian prior for the regression coefficients. Larger values imply more shrinkage or a more concentrated prior for the regresion coefficients.
Iteration control parameter
Logical, include the Hessian in the output?
names to use as dimnames(mode.mat)[[2]]
Not usually set by the user. A matrix which indicates the values of regressor
variables corresponding to the allele states. If mode.mat is
not given by the user,
ana.obj$mode.mat is used.
Maximum Number of iterations to perform
Number of EM iterations to use in reinitializing the pseudo-Hessian
If TRUE, do not run. Return an object that can be use
for a direct call to .C
expression to evaluate using ana.obj$data as the
environment
a vector of non-negative weights
Vector of starting values for the maximization
other objects needed in fitting
Charles C. Berry cberry@ucsd.edu
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.
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.