=======================================================
 Starting Values and Fine Tuning of the MCEM algorithm 
=======================================================

.. r-package:: manifestos
.. r-pkgversion:: 0.8
.. r-name:: latpos_tunefit

     latpos.control
     latpos.start

Description
===========

Function ``latpos.start`` is used to construct "good" starting values, while function
``latpos.control`` provides settings for the numerical aspects of the MCEM algorithm,
with reasonable defaults.

Usage
=====

.. code-block:: r

     latpos.start(resp,latent.dims,manifest,start,
                  unfold.method,restrictions=standard.restrictions,
                  maxiter,...)
     latpos.control(maxiter=200,initial.size=101,
                               Lambda.alpha=.05,
                               Lambda.eps=1e-7,
                               diff.logLik.eps=1e-7,
                               abs.diff.psi.eps=0,
                               rel.diff.psi.eps=0,
                               max.size=Inf,
                               min.final.size=1000,
                               force.increase=TRUE,
                               Q.linesearch=TRUE,
                               ...)

Arguments
=========

``resp``
   an internal representation of the observed data.

``latent.dims``
   a character vector with the names of the axes of the latent space.

``manifest``
   a character vector with the names of the observed variables, i.e. emphasis counts of
   policy objectives.

``start``
   an optional list with starting values for the model parameters

``unfold.method``
   the unfolding method to be used to generate reasonable starting values.

``restrictions``
   an object representing restrictions on the model parameters, see ``restrictor``.

``maxiter``
   the maximum number of iterations to use, in ``latpos.start`` to get initial values for
   the posterior modes, in ``latpos.control`` to set the maximum number of MCEM iterations.

``initial.size``
   a positive number, the simulation sample size to use in the first MCEM iteration.

``Lambda.alpha``
   a "significance level" for the increase of the Q-function. If the increase is not
   "statistically significant" at this level, the sample size is automatically increased.

``Lambda.eps``
   a non-negative number as convergence critierion.  If the increase of the Q-function is
   smaller than this value, convergence of the MCEM is declared.

``diff.logLik.eps``
   a non-negative number as convergence critierion.  If the increase of the marginal
   log-likelihood is smaller than this value, convergence of the MCEM is declared.

``abs.diff.psi.eps``
   a non-negative number as an alternative convergence critierion.  if the absolute change
   of the model parameters is smaller than this value, convergence of the MCEM is declared.

``rel.diff.psi.eps``
   a non-negative number as an alternative convergence critierion.  if the absolute change
   of the model parameters is smaller than this value, convergence of the MCEM is declared.

``max.size``
   a positive number, the maximum simulation sample size to be used.

``min.final.size``
   a positive number, the minimal simulation sample size to be used in the final iterations
   of the MCEM algorithm.

``force.increase``
   logical; if TRUE and the likelihood or the Q-function cannot be increased then conduct a
   line search for the optimal step size.

``Q.linesearch``
   logical; if TRUE, force.increase==TRUE and the likelihood or the Q-function cannot be
   increased then conduct a line search for the optimal step size; if FALSE, but
   force.increase==TRUE and the likelihood or the Q-function cannot be increased then step
   back to the values of the previous iteration.

``...``
   other arguments, ignored.