View source: R/08_regmedint_class_user_methods.R
| summary.regmedint | R Documentation | 
Summarize the mreg_fit, yreg_fit, and the mediation analysis effect estimates.
## S3 method for class 'regmedint'
summary(
  object,
  a0 = NULL,
  a1 = NULL,
  m_cde = NULL,
  c_cond = NULL,
  args_mreg_fit = list(),
  args_yreg_fit = list(),
  exponentiate = FALSE,
  level = 0.95,
  ...
)
object | 
 An object of the   | 
a0 | 
 A numeric vector of length 1  | 
a1 | 
 A numeric vector of length 1  | 
m_cde | 
 A numeric vector of length 1 The mediator value at which the controlled direct effect (CDE) conditional on the adjustment covariates is evaluated. If not provided, the default value supplied to the call to   | 
c_cond | 
 A numeric vector of the same length as   | 
args_mreg_fit | 
 A named list of argument to be passed to the method for the   | 
args_yreg_fit | 
 A named list of argument to be passed to the method for the   | 
exponentiate | 
 Whether to add exponentiated point and confidence limit estimates. When   | 
level | 
 Confidence level for the confidence intervals.  | 
... | 
 For compatibility with the generic. Ignored.  | 
A summary_regmedint object, which is a list containing the summary objects of the mreg_fit and the yreg_fit as well as the mediation analysis results.
library(regmedint)
data(vv2015)
regmedint_obj <- regmedint(data = vv2015,
                           ## Variables
                           yvar = "y",
                           avar = "x",
                           mvar = "m",
                           cvar = c("c"),
                           eventvar = "event",
                           ## Values at which effects are evaluated
                           a0 = 0,
                           a1 = 1,
                           m_cde = 1,
                           c_cond = 0.5,
                           ## Model types
                           mreg = "logistic",
                           yreg = "survAFT_weibull",
                           ## Additional specification
                           interaction = TRUE,
                           casecontrol = FALSE)
## Detailed result with summary
summary(regmedint_obj)
## Add exponentiate results for non-linear outcome models
summary(regmedint_obj, exponentiate = TRUE)
## Evaluate at different values
summary(regmedint_obj, m_cde = 0, c_cond = 1)
## Change confidence level
summary(regmedint_obj, m_cde = 0, c_cond = 1, level = 0.99)
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