print method for regmedint object

Print the mreg_fit, yreg_fit, and the mediation analysis effect estimates.

# S3 method for regmedint
print(
  x,
  a0 = NULL,
  a1 = NULL,
  m_cde = NULL,
  c_cond = NULL,
  args_mreg_fit = list(),
  args_yreg_fit = list(),
  ...
)

Arguments

x

An object of the regmedint class.

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 regmedint will be used. Only the CDE is affected.

c_cond

A numeric vector of the same length as cvar. A set of covariate values at which the conditional natural effects are evaluated.

args_mreg_fit

A named list of argument to be passed to the method for the mreg_fit object.

args_yreg_fit

A named list of argument to be passed to the method for the mreg_fit object.

...

For compatibility with the generic. Ignored.

Value

Invisibly return the regmedint class object as is.

Examples

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)
## Implicit printing
regmedint_obj
#> ### Mediator model
#> 
#> Call:  glm(formula = m ~ x + c, family = binomial(link = "logit"), data = data)
#> 
#> Coefficients:
#> (Intercept)            x            c  
#>     -0.3545       0.3842       0.2694  
#> 
#> Degrees of Freedom: 99 Total (i.e. Null);  97 Residual
#> Null Deviance:	    138.6 
#> Residual Deviance: 136.1 	AIC: 142.1
#> ### Outcome model
#> Call:
#> survival::survreg(formula = Surv(y, event) ~ x + m + x:m + c, 
#>     data = data, dist = "weibull")
#> 
#> Coefficients:
#> (Intercept)           x           m           c         x:m 
#> -1.04244118  0.44075656  0.09053705 -0.06689165  0.10031424 
#> 
#> Scale= 0.9658808 
#> 
#> Loglik(model)= -11.4   Loglik(intercept only)= -14.5
#> 	Chisq= 6.31 on 4 degrees of freedom, p= 0.177 
#> n= 100 
#> ### Mediation analysis 
#>         cde        pnde        tnie        tnde        pnie          te 
#> 0.541070807 0.488930417 0.018240025 0.498503455 0.008666987 0.507170442 
#>          pm 
#> 0.045436278 
## Explicit printing
print(regmedint_obj)
#> ### Mediator model
#> 
#> Call:  glm(formula = m ~ x + c, family = binomial(link = "logit"), data = data)
#> 
#> Coefficients:
#> (Intercept)            x            c  
#>     -0.3545       0.3842       0.2694  
#> 
#> Degrees of Freedom: 99 Total (i.e. Null);  97 Residual
#> Null Deviance:	    138.6 
#> Residual Deviance: 136.1 	AIC: 142.1
#> ### Outcome model
#> Call:
#> survival::survreg(formula = Surv(y, event) ~ x + m + x:m + c, 
#>     data = data, dist = "weibull")
#> 
#> Coefficients:
#> (Intercept)           x           m           c         x:m 
#> -1.04244118  0.44075656  0.09053705 -0.06689165  0.10031424 
#> 
#> Scale= 0.9658808 
#> 
#> Loglik(model)= -11.4   Loglik(intercept only)= -14.5
#> 	Chisq= 6.31 on 4 degrees of freedom, p= 0.177 
#> n= 100 
#> ### Mediation analysis 
#>         cde        pnde        tnie        tnde        pnie          te 
#> 0.541070807 0.488930417 0.018240025 0.498503455 0.008666987 0.507170442 
#>          pm 
#> 0.045436278 
## Evaluate at different values
print(regmedint_obj, m_cde = 0, c_cond = 1)
#> ### Mediator model
#> 
#> Call:  glm(formula = m ~ x + c, family = binomial(link = "logit"), data = data)
#> 
#> Coefficients:
#> (Intercept)            x            c  
#>     -0.3545       0.3842       0.2694  
#> 
#> Degrees of Freedom: 99 Total (i.e. Null);  97 Residual
#> Null Deviance:	    138.6 
#> Residual Deviance: 136.1 	AIC: 142.1
#> ### Outcome model
#> Call:
#> survival::survreg(formula = Surv(y, event) ~ x + m + x:m + c, 
#>     data = data, dist = "weibull")
#> 
#> Coefficients:
#> (Intercept)           x           m           c         x:m 
#> -1.04244118  0.44075656  0.09053705 -0.06689165  0.10031424 
#> 
#> Scale= 0.9658808 
#> 
#> Loglik(model)= -11.4   Loglik(intercept only)= -14.5
#> 	Chisq= 6.31 on 4 degrees of freedom, p= 0.177 
#> n= 100 
#> ### Mediation analysis 
#>         cde        pnde        tnie        tnde        pnie          te 
#> 0.440756562 0.492306223 0.018077074 0.501765186 0.008618111 0.510383297 
#>          pm 
#> 0.044816400