mra (version 2.16.4)

mra.control: mra.control - Control over MRA fitting process

Description

Auxiliary function providing a user interface for mra fitting. Typically only used when calling one of the *.estim routines.

Usage

mra.control(algorithm=1, maxfn=1000, cov.meth=1, trace=0, tol=1e-07 )

Arguments

algorithm

Integer specifying the maximization algorithm to use. If algorithm = 1, the VA09AD algorithm from the HSL library is used. The VA09AD algorithm is very reliable, has been tested extensively (same algorithm as Program MARK), and will almost always find the maximum likelihood estimates. This parameter was added to allow easy addition of other algorithms in the future, but no other options are currently implemented because VA09AD works so well. Anything other than 1 throws a warning and resets the value to 1.

maxfn

Maximum number of likelihood evaluations allowed during the fitting process. This determines when the minimization routine stops and concludes that the problem will not converge. The routine stops after the likelihood has been evaluated maxfn times within the minimization routine (but not the hessian or gradient routine). Decreasing this value will cause the fitting routine to give up on badly-behaved models quicker and return faster. In some simulation where ill-conditioned data can be generated, decreasing this parameter can speed up the simulation tremendously. Increasing this value may allow marginally-behaved (slow to converge) models to converge.

cov.meth

Integer specifying the covariance estimation method. cov.meth = 1 takes numerical 2nd partial derivatives. cov.meth = 2 inverts the Hessian of the maximization. Method 1 (numeric 2nd derivatives) is the preferred method, but is computationally expensive and therefore slower than method 2. The difference in speed is minimal when number of parameters < 15. Method 2 variances are generally very close to method 1 variances, and could be used during initial model fitting stages when exact estimation of variance may not be necessary.

trace

Integer controling output of intermediate results. If trace != 0, a few lines will be written to the R console and a log file (named mra.log) will be opened in the current directory and details of the fitting process will be written there. The level of detail written to the log depends on the value of trace. If trace == 1, details written to the log include TEST2 and TEST3 results used to determine c-hat, details from the fitting algorithm, the variance covarince matrix, singular values of the VC matrix used to determine df, and other statistics. If trace == 2, all trace == 1 details plus the likelihood output is written to the log. If trace == 3, all trace == 2 details plus the gradient is written to the log. When using algorithm = 1 (VA09AD), trace has additional meaning. If trace > 3, the coefficient and gradient vectors are written every |trace| iterations and also on exit. Output is of the form: Function value, beta(1), beta(2), ..., beta(n), G(1), ...G(n). Coefficient and gradient values are suppressed if trace < 0 (only final results printed). Intermediate values from within VA09AD are suppressed if trace > maxit + 1, but other intermediate values are output. trace > 1 probably produces more output than the casual (non-geek statistician) wants to look at. But, geek statisticans may find trace > 3 useful for assessing stability and determining when the routine gets stuck in a local minima.

tol

Vector or scalar of tolerance(s) for coefficients in the model. Minimization stops and concludes it has converged when |delta.b(i)| < tol(i) for all i, where delta.b(i) is change in parameter i on an iteration.

Value

A list with the input arguments as components.

See Also

F.cjs.estim, F.huggins.estim

Examples

data(dipper.histories)
ct <- as.factor( paste("T",1:ncol(dipper.histories), sep=""))
attr(ct,"nan")<-nrow(dipper.histories)
dipper.cjs <- F.cjs.estim( ~tvar(ct,drop=c(1,2)), ~tvar(ct,drop=c(1,6,7)), 
  dipper.histories, control=mra.control(trace=1, maxfn=200) )