glmc.control: Auxiliary for Controlling GLM Fitting with population level...
In glmc: Fitting Generalized Linear Models Subject to Constraints

Description Usage Arguments Value

Auxiliary function as user interface for glmc fitting. Typically only used when calling glmc.

glmc.control(epsilon.glm = 1e-8, maxit.glm= 100, trace.glm= FALSE, 
             trace.optim= 0, fnscale.optim=-1, parscale.optim = rep.int(1,1), 
             ndeps.optim = rep.int(0.001,1), maxit.optim = 100, 
             abstol.optim = -Inf, reltol.optim= sqrt(.Machine$double.eps), 
             alpha.optim = 1, beta.optim = 0.5, gamma.optim = 2, 
             REPORT.optim= 10, type.optim = 1, lmm.optim = 5, 
             factr.optim= 1e+07, pgtol.optim = 0, tmax.optim = 10, 
             temp.optim =10, maxit.weights = 25, gradtol.weights = 1e-07, 
             svdtol.weights = 1e-09, itertrace.weights = FALSE)

`epsilon.glm`	positive convergence tolerance epsilon; the iterations converge when \|dev - devold\|/(\|dev\| + 0.1) < epsilon.
`maxit.glm`	integer giving the maximal number of IWLS iterations.
`trace.glm`	logical indicating if output should be produced for each iteration.
`trace.optim`	Non-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method “L-BFGS-B” there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.)
`fnscale.optim`	A negative number determining the overall scaling to be applied to the value of fn and gr during optimization. In `glmc` optimization is performed on fn(par)/(fnscale.optim) if fnscale.optim is negative and on fn(par)/((-1)fnscale.optim)* if fnscale.optim is positive.
`parscale.optim`	A vector of scaling values for the parameters. Optimization is performed on par/parscale and these should be comparable in the sense that a unit change in any element produces about a unit change in the scaled value.
`ndeps.optim`	A vector of step sizes for the finite-difference approximation to the gradient, on par/parscale scale. Defaults to 1e-3.
`maxit.optim`	The maximum number of iterations. Defaults to 100 for the derivative-based methods, and 500 for “Nelder-Mead”. For “SANN” maxit gives the total number of function evaluations. There is no other stopping criterion. Defaults to 10000.
`abstol.optim`	The absolute convergence tolerance. Only useful for non-negative functions, as a tolerance for reaching zero.
`reltol.optim`	Relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of reltol * (abs(val) + reltol) at a step. Defaults to sqrt(.Machine\$double.eps), typically about 1e-8.
`alpha.optim, beta.optim, gamma.optim`	Scaling parameters for the “Nelder-Mead” method. alpha is the reflection factor (default 1.0), beta the contraction factor (0.5) and gamma the expansion factor (2.0).
`REPORT.optim`	The frequency of reports for the “BFGS” and “L-BFGS-B” methods if control\$trace is positive. Defaults to every 10 iterations.
`type.optim`	for the conjugate-gradients method. Takes value 1 for the Fletcher–Reeves update, 2 for Polak–Ribiere and 3 for Beale–Sorenson.
`lmm.optim`	is an integer giving the number of BFGS updates retained in the “L-BFGS-B” method, It defaults to 5.
`factr.optim`	controls the convergence of the “L-BFGS-B” method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1e-8.
`pgtol.optim`	helps controls the convergence of the “L-BFGS-B” method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed.
`temp.optim`	controls the “SANN” method. It is the starting temperature for the cooling schedule. Defaults to 10.
`tmax.optim`	is the number of function evaluations at each temperature for the “SANN” method. Defaults to 10.
`maxit.weights`	an optional integer to control iteration when solve constrained maximisation for the weights.
`gradtol.weights`	an optional real value for convergence test while calculating the weights.
`svdtol.weights`	an optional real value to detect singularity while solve equations. This is used to compute the weights.
`itertrace.weights`	a logical value. If the iteration history when calculating the weights needs to be printed out.