Extract association rules from an object created by the createdatadiscrete function, using discrete time regression models to assess the significance of the extracted rules.
seqerulesdisc(fsubseq, datadiscr, tsef, pvalue=0.1, supvars=NULL,
adjust=TRUE, topt=FALSE, link="cloglog", dep=NULL)an object created using the seqefsub function and that contains the list of subsequences to be tested for an association
the object created by the createdatadiscrete function and that contains the person-period data
the data frame containing the original time-to-event dataset (equivalent to the data argument from the createdatadiscrete function)
the default threshold p-value to consider an association rule as significative, default is 0.1
a vector of variable names to be used as control variables in the regression models (experimental)
if set to TRUE, a Bonferroni adjustment is applied to the p-value threshold specified in the pvalue argument
if set to TRUE, use an alternative algorithm to extract the rules (very experimental) ; default to FALSE
the link function to be used in the generalized linear regression model. To obtain hazard ratios, use the complementary log-log link function ("cloglog", as default). The other choice is to use a logit link function ("logit").
if set to NULL, test all possible association rules. If an event is set, the function will only test association rules ending with this event
This function uses a list of subsequences created by the seqefsub function from the TraMineR package and tests each possible association rules. It then shows the association rules whose significance, assessed using a discrete time regression model, is higher than the specified p-value threshold.
The algorithm is described in the M<U+2CB25CA0>et al. (2010) article, even though this function uses a discrete time regression model instead of the Cox regression model described in the article. A more complete explanation of the method is available in M<U+2CB25CA0>(2011).