R: Stepwise model selection in RCOX models

step {gRc}

R Documentation

Stepwise model selection in RCOX models

Description

These allow for stepwise model selection in RCOX models by. Model expansion (i.e. forward selection) is obtained by adding edge colour classes and by splitting edge/vertex colour classes. Model reduction (i.e. backward selection) is obtained by dropping edge colour classes and by joining edge/vertex colour classes.

Arguments

`object`	An RCOX model, an object of class RCOX
`scope`	A set (list) of items (edge colour classes or vertex colour classes) to be considered. If missing, then all items are considered.
`criterion`	Either "aic" (the default), "bic" or "test" (for significance test)
`type`	Either "ecc" for edge colour classes or "vcc" for vertex colour classes.
`k`	The multiple of the number of degrees of freedom used for the penalty when criterion is "aic". Ignored when criterion is "bic" or "test". Only k = 2 gives the genuine AIC.
`steps`	The maximum number of steps to be considered. The default is 1000 (essentially as many as required). It is typically used to stop the process early
`stat`	Either "wald" for a Wald statistic or "dev" for a deviance statistic.
`alpha`	Critical value if 'criterion' is "test". If criterion is "aic" or "bic", the critical value is 0.
`headlong`	If TRUE then at each step the first encountered edge that may be removed/added according to the current criterion is done so.
`random`	If TRUE, then the edges are examined in random order
`details`	Control the amount of output created.
`trace`	For debugging purposes
`...`	Additional arguments, currently not used.

Value

Either NULL or a new RCOX model.

Note

Note that the keyword 'stat' is not available for stepadd1 and stepsplit1 because these functions expand the current model and hence the Wald statistic is not available.

Author(s)