sema_fit_set: Fit multilevel models in a data stream II
In L-Ippel/SEMA: Streaming Expectation Maximization Approximation

Description Usage Arguments Details Value See Also Examples

Fit multilevel models in a data stream, without managing units' objects lists,

sema_fit_set(data_fixed, data_random, data_y, id, theta_list = list(theta
  = create_theta_main(n_fixed = length(data_fixed), n_random =
  length(data_random)), id_vector = c(), theta_j = NULL), print = FALSE,
  update = NULL, start_resid_var = 1, start_random_var = 1,
  start_fixed_coef = NULL, prior_n = 0, prior_j = 0)

`data_fixed`	A vector with the data of the fixed effects covariates.
`data_random`	A vector with the data of the random effects covariates.
`data_y`	A scalar with the response of this unit.
`id`	A scalar which identifies the unit of this data point.
`theta_list`	Alist consisting of 'theta', which is a list with all model parameters and global sufficient statistics; 'id_vector' which is a vector containig all id labels; and 'theta_j' which is a list of lists containing all units parameters and contributions to the sufficient statistics. This list is automatically generated.
`print`	The default is FALSE, if TRUE the function prints a summary of the model.
`update`	The default is NULL, when an integer is provided `sema_update` is called to do a full update to recompute all contributions to the complete data suffient statistics.
`start_resid_var`	A scalar, optional if the user wants to provide a start value of the residual variance, default start value is 1.
`start_random_var`	A vector, 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.
`start_fixed_coef`	A vector, optional if the user wants to provide start values of the fixed effects, default is set to NULL such that `sema_fit_one` creates the vector of start values matching the number of fixed effects. NOTE, if start values are provided make sure that the length of the vector of start values matches the number of fixed effects.
`prior_n`	If starting values are provided, prior_n determines the weight of the starting value of the residual variance, default is 0.
`prior_j`	If starting values are provided, prior_j determines the weight of the starting value of the variance of the random effects and the fixed effects, default is 0.

This function fits the multilevel models in a data stream, similar to sema_fit_one. However, while sema_fit_one does not manage the storage and retrieval of units' objects, this function does, which makes this function more user-friendly. The function requires an id label, to retrieve the corresponding unit's parameters, a vector with the data of the fixed effects covariates, a vector with the data of the random effects covariates, the response or dependent variable and the current state of the model parameters, including the lists with the units' objects. Currently the algorithm fits models including fixed effects at level 1 and 2 and random intercepts and slopes for continuous outcomes.

A list with updated global parameters, a vector with id labels and a list with lists of all units parameters and contributions.

sema_fit_one, sema_fit_df, summary_sema, ranef, store_resid_var, store_random_var, store_fixed_coef

## First we create a dataset, consisting of 2500 observations from 20 
## units. The fixed effects have the coefficients 1, 2, 3, 4, and 5. The 
## variance of the random effects equals 1, 4, and 9. Lastly the 
## residual variance equals 4:
test_data <- build_dataset(n = 1500, 
                           j = 200, 
                           fixed_coef = 1:5, 
                           random_coef_sd = 1:3, 
                           resid_sd = 2)
## to simplify the indexing, we generate 2 vectors, one that indicates which
## columns are fixed effects variables and the other to indicate in which
## columns the random effects variables are

data_fixed_var <- c(3:7)
data_random_var <- c(3,5,6)

## an object where fit_sema output is stored in, this should be \code{NULL}
## because that tells the fit_sema function to create model statistics lists 

m1 <- NULL

## looping though the dataset like this: 
for(i in 1:nrow(test_data)){
m1 <- sema_fit_set(data_fixed = test_data[i, data_fixed_var],
                   data_random = test_data[i, data_random_var],
                   data_y = test_data$y[i],
                   id = test_data$id[i],
                   theta_list = m1, 
                   print = FALSE,
                   update = NULL)
}