- formula
 
A formula expression response ~ predictors;
- id
 
A vector for identifying subjects/clusters.
- data
 
A data frame which stores the variables in formula with id variable.
- na.action
 
A function to remove missing values from the data. Only na.omit is allowed here.
- family
 
A family object: a list of functions and expressions for defining link and
variance functions. Families supported in PGEE are binomial, gaussian, gamma and
poisson. The links, which are not available in gee, is not available here. The default family
is gaussian.
- corstr
 
A character string, which specifies the correlation of correlation structure.
Structures supported in PGEE are "AR-1","exchangeable", "fixed", "independence",
"stat_M_dep","non_stat_M_dep", and "unstructured". The default corstr correlation is
"independence".
- Mv
 
If either "stat_M_dep", or "non_stat_M_dep" is specified in corstr, then this
assigns a numeric value for Mv. Otherwise, the default value is NULL.
- beta_int
 
User specified initial values for regression parameters. The default value is NULL.
- R
 
If corstr = "fixed" is specified, then R is a square matrix of dimension maximum cluster
size containing the user specified correlation. Otherwise, the default value is NULL.
- scale.fix
 
A logical variable; if true, the scale parameter is fixed at the value of scale.value.
The default value is TRUE.
- scale.value
 
If scale.fix = TRUE, this assigns a numeric value to which the scale parameter should be
fixed. The default value is 1.
- lambda
 
A numerical value for the penalization parameter of the scad function, which is estimated via
cross-validation.
- pindex
 
An index vector showing the parameters which are not subject to penalization. The default value
is NULL. However, in case of a model with intercept, the intercept parameter should be never penalized.
- eps
 
A numerical value for the epsilon used in minorization-maximization algorithm. The default value is
10^-6.
- maxiter
 
The number of iterations that is used in the estimation algorithm. The default value is 25.
- tol
 
The tolerance level that is used in the estimation algorithm. The default value is 10^-3.
- silent
 
A logical variable; if false, the regression parameter estimates at each iteration are
printed. The default value is TRUE.