Print results contained in a summary_regmedint object with additional explanation regarding the evaluation settings.

# S3 method for summary_regmedint
print(x, ...)

Arguments

x

An object of the class summary_regmedint.

...

For compatibility with the generic function.

Value

Invisibly return the first argument.

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
summary(regmedint_obj)
#> ### Mediator model
#> 
#> Call:
#> glm(formula = m ~ x + c, family = binomial(link = "logit"), data = data)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -1.5143  -1.1765   0.9177   1.1133   1.4602  
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept)  -0.3545     0.3252  -1.090    0.276
#> x             0.3842     0.4165   0.922    0.356
#> c             0.2694     0.2058   1.309    0.191
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 138.59  on 99  degrees of freedom
#> Residual deviance: 136.08  on 97  degrees of freedom
#> AIC: 142.08
#> 
#> Number of Fisher Scoring iterations: 4
#> 
#> ### Outcome model
#> 
#> Call:
#> survival::survreg(formula = Surv(y, event) ~ x + m + x:m + c, 
#>     data = data, dist = "weibull")
#>               Value Std. Error     z       p
#> (Intercept) -1.0424     0.1903 -5.48 4.3e-08
#> x            0.4408     0.3008  1.47    0.14
#> m            0.0905     0.2683  0.34    0.74
#> c           -0.0669     0.0915 -0.73    0.46
#> x:m          0.1003     0.4207  0.24    0.81
#> Log(scale)  -0.0347     0.0810 -0.43    0.67
#> 
#> Scale= 0.966 
#> 
#> Weibull distribution
#> Loglik(model)= -11.4   Loglik(intercept only)= -14.5
#> 	Chisq= 6.31 on 4 degrees of freedom, p= 0.18 
#> Number of Newton-Raphson Iterations: 5 
#> n= 100 
#> 
#> ### Mediation analysis 
#>              est         se         Z          p       lower      upper
#> cde  0.541070807 0.29422958 1.8389409 0.06592388 -0.03560858 1.11775019
#> pnde 0.488930417 0.21049248 2.3227928 0.02019028  0.07637274 0.90148809
#> tnie 0.018240025 0.03706111 0.4921608 0.62260566 -0.05439841 0.09087846
#> tnde 0.498503455 0.21209540 2.3503737 0.01875457  0.08280410 0.91420281
#> pnie 0.008666987 0.02730994 0.3173565 0.75097309 -0.04485951 0.06219348
#> te   0.507170442 0.21090051 2.4047853 0.01618197  0.09381303 0.92052785
#> pm   0.045436278 0.09119614 0.4982259 0.61832484 -0.13330488 0.22417743
#> 
#> Evaluated at:
#> avar: x
#>  a1 (intervened value of avar) = 1
#>  a0 (reference value of avar)  = 0
#> mvar: m
#>  m_cde (intervend value of mvar for cde) = 1
#> cvar: c
#>  c_cond (covariate vector value) = 0.5
#> 
#> Note that effect estimates can vary over m_cde and c_cond values when interaction = TRUE.
## Explicit printing
print(summary(regmedint_obj))
#> ### Mediator model
#> 
#> Call:
#> glm(formula = m ~ x + c, family = binomial(link = "logit"), data = data)
#> 
#> Deviance Residuals: 
#>     Min       1Q   Median       3Q      Max  
#> -1.5143  -1.1765   0.9177   1.1133   1.4602  
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)
#> (Intercept)  -0.3545     0.3252  -1.090    0.276
#> x             0.3842     0.4165   0.922    0.356
#> c             0.2694     0.2058   1.309    0.191
#> 
#> (Dispersion parameter for binomial family taken to be 1)
#> 
#>     Null deviance: 138.59  on 99  degrees of freedom
#> Residual deviance: 136.08  on 97  degrees of freedom
#> AIC: 142.08
#> 
#> Number of Fisher Scoring iterations: 4
#> 
#> ### Outcome model
#> 
#> Call:
#> survival::survreg(formula = Surv(y, event) ~ x + m + x:m + c, 
#>     data = data, dist = "weibull")
#>               Value Std. Error     z       p
#> (Intercept) -1.0424     0.1903 -5.48 4.3e-08
#> x            0.4408     0.3008  1.47    0.14
#> m            0.0905     0.2683  0.34    0.74
#> c           -0.0669     0.0915 -0.73    0.46
#> x:m          0.1003     0.4207  0.24    0.81
#> Log(scale)  -0.0347     0.0810 -0.43    0.67
#> 
#> Scale= 0.966 
#> 
#> Weibull distribution
#> Loglik(model)= -11.4   Loglik(intercept only)= -14.5
#> 	Chisq= 6.31 on 4 degrees of freedom, p= 0.18 
#> Number of Newton-Raphson Iterations: 5 
#> n= 100 
#> 
#> ### Mediation analysis 
#>              est         se         Z          p       lower      upper
#> cde  0.541070807 0.29422958 1.8389409 0.06592388 -0.03560858 1.11775019
#> pnde 0.488930417 0.21049248 2.3227928 0.02019028  0.07637274 0.90148809
#> tnie 0.018240025 0.03706111 0.4921608 0.62260566 -0.05439841 0.09087846
#> tnde 0.498503455 0.21209540 2.3503737 0.01875457  0.08280410 0.91420281
#> pnie 0.008666987 0.02730994 0.3173565 0.75097309 -0.04485951 0.06219348
#> te   0.507170442 0.21090051 2.4047853 0.01618197  0.09381303 0.92052785
#> pm   0.045436278 0.09119614 0.4982259 0.61832484 -0.13330488 0.22417743
#> 
#> Evaluated at:
#> avar: x
#>  a1 (intervened value of avar) = 1
#>  a0 (reference value of avar)  = 0
#> mvar: m
#>  m_cde (intervend value of mvar for cde) = 1
#> cvar: c
#>  c_cond (covariate vector value) = 0.5
#> 
#> Note that effect estimates can vary over m_cde and c_cond values when interaction = TRUE.