emAlgorithm: Fit Multilevel model with EM
In L-Ippel/SEMA: Streaming Expectation Maximization Approximation

To provide SEMA with good starting values, it might help to use the first part of the data as a training set.

emAlgorithm(data, id, y, start.fixed = c(90, rnorm(13)),
  start.random = runif(5, 0, 4), data.random = c(3, 9:12),
  start.cor = 0.15, start.res = 1, max.iter = 20,
  crit.value = 1e-04)

`data`	A data frame
`id`	Integer, indicating in which column the grouping variable can be found.
`y`	Integer, indicating in which column the dependent variable can be found.
`start.fixed`	This is optional if the user wants to provide start values of the fixed effects, if no start values are provided, 0 is used. NOTE, if start values are provided make sure that the length of the vector of start values matches the number of fixed effects.
`start.random`	This is optional if the user wants to provide a start values of the variance of the random effects covariates, default start value is 1. NOTE, if start values are provided make sure that the length of the vector of start values matches the number of random effects.
`data.random`	A vector with indexes of the variables treated as random.
`start.cor`	Start value for correlation between the random effects.
`start.res`	This is optional if the user wants to provide a start value of the residual variance, default start value is 1.
`max.iter`	Integer, maximum number of iterations for EM algorithm.
`crit.value`	A threshold or stopping criterion, if the maximum change in parameter values from one iteration to the next is less than this value, the algorithm stops.