![[Experimental]](figures/lifecycle-experimental.svg)
When using categorical interaction terms in a mdl_tbl object, estimates
on interaction terms and their confidence intervals can be evaluated. The
effect of interaction on the estimates is based on the levels of interaction
term. The estimates and intervals can be derived through the
estimate_interaction() function. The approach is based on the method
described by Figueiras et al. (1998).
Arguments
- object
A
mdl_tblobject subset to a single row- exposure
The exposure variable in the model
- interaction
The interaction variable in the model
- conf_level
The confidence level for the confidence interval
- ...
Arguments to be passed to or from other methods
Value
A data.frame with n = levels(interaction) rows (for the
presence or absence of the interaction term) and n = 5 columns:
estimate: beta coefficient for the interaction effect based on level
conf_low: lower bound of confidence interval for the estimate
conf_high: higher bound of confidence interval for the estimate
p_value: p-value for the overall interaction effect across levels
nobs: number of observations within the interaction level
level: level of the interaction term
Details
The estimate_interaction() requires a mdl_tbl object that is a
single row in length. Filtering the mdl_tbl should occur prior to
passing it to this function. Additionally, this function assumes the
interaction term is binary. If it is categorical, the current
recommendation is to use dummy variables for the corresponding levels prior
to modeling.