# Starting Values and Fine Tuning of the MCEM algorithm¶

## 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¶

```
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.