# \donttest{
data(EFV)
# first: generate generic model formulas
m <- make.model.formulas(X=EFV,
Lnodes = c("adherence.1","weight.1",
"adherence.2","weight.2",
"adherence.3","weight.3",
"adherence.4","weight.4"
),
Ynodes = c("VL.0","VL.1","VL.2","VL.3","VL.4"),
Anodes = c("efv.0","efv.1","efv.2","efv.3","efv.4"),
evaluate=FALSE)
# second: update these model formulas based on variable screening with LASSO
glmnet.formulas <- model.formulas.update(m$model.names, EFV)
glmnet.formulas
# then: fit and inspect the updated models
fitted.models <- fit.updated.formulas(glmnet.formulas, EFV)
fitted.models$all.summaries
fitted.models$all.summaries$Ynames[1] # first outcome model
# }
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