loop_lss is the EM loop function for the LSS model to be utilised by various other higher level functions
loop_lss(
alldat,
xiold,
omega2old,
nuold,
mean.ind,
var.ind,
nu.ind,
para.space,
maxit,
eps,
int.maxit,
print.it
)A list of the results from the algorithm, including conv, reldiff, it, mean, xi.new, omega2.new, nu.new, fitted.xi
conv: Logical argument indicating if convergence occurred
it: Total iterations performed of the EM algorithm
reldiff: the positive convergence tolerance that occured at the final iteration
xinew: Vector of location parameter estimates
omega2new: Vector of scale squared parameter estimates
nunew: Vector of shape parameter estimates
fitted.xi: Vector of fitted location estimates
Dataframe containing all the data for the models. Outcome in the first column.
Vector of initial location parameter estimates to be fit in the location model.
Vector of initial scale2 parameter estimates to be fit in the scale2 model.
Vector of initial nu parameter estimates to be fit in the nu model.
Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the location model.
Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the scale2 model. FALSE indicates a constant variance model.
Vector containing the column numbers of the data in 'alldat' to be fit as covariates in the nu model. NULL indicates constant model.
Parameter space to search for variance parameter estimates. "positive" means only search positive parameter space, "negative" means search only negative parameter space and "all" means search all.
Number of maximum iterations for the main EM algorithm.
Very small number for the convergence criteria.
Number of maximum iterations for the internal EM algorithm for the location and scale.
Logical to indicate if the estimates for each iteration should be printed.