Mediation Analysis

Source: R/mediation_summary.R

[Experimental]
It currently only support simple mediation analysis using the path analysis approach with the lavaan package. I am trying to implement multilevel mediation in lavaan. In the future, I will try supporting moderated mediation (through lavaan or mediation) and mediation with latent variable (through lavaan).

mediation_summary(
  data,
  response_variable,
  mediator,
  predictor_variable,
  control_variable = NULL,
  group = NULL,
  standardize = TRUE,
  digits = 3,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

data

data.frame

response_variable

response variable. Support dplyr::select() syntax.

mediator

mediator. Support dplyr::select() syntax.

predictor_variable

predictor variable. Support dplyr::select() syntax.

control_variable

control variables / covariate. Support dplyr::select() syntax.

group

nesting variable for multilevel mediation. Not confident about the implementation method. [Experimental]

standardize

standardized coefficients. Default is TRUE

digits

number of digits to round to

quite

suppress printing output

streamline

print streamlined output

return_result

If it is set to TRUE, it will return the lavaan object

Value

an object from lavaan

Examples

mediation_summary(
  data = lmerTest::carrots,
  response_variable = Preference,
  mediator = Sweetness,
  predictor_variable = Crisp
)
#> Warning: The following columns are coerced into numeric: Consumer, Frequency, Gender, Age, Homesize, Work, Income, Product
#> Model Summary
#> Model Type = Mediation Analysis (fitted using lavaan)
#> 
#> Effect Summary
#> ────────────────────────────────────────────────────────────────
#>   Effect Type  Est.Std     SE       z          p          95% CI
#> ────────────────────────────────────────────────────────────────
#>        direct    0.197  0.029   6.880  0.000 ***  [0.141, 0.253]
#>      indirect    0.219  0.017  12.656  0.000 ***  [0.185, 0.253]
#>         total    0.416  0.023  18.444  0.000 ***  [0.372, 0.460]
#> ────────────────────────────────────────────────────────────────
#> *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1
#> 
#> Regression Summary
#> ────────────────────────────────────────────────────────────────────────────────────
#>     Response  Operator    Predict  Est.Std     SE       z          p          95% CI
#> ────────────────────────────────────────────────────────────────────────────────────
#>    Sweetness         ~      Crisp    0.558  0.018  30.893  0.000 ***  [0.523, 0.593]
#>   Preference         ~  Sweetness    0.392  0.028  14.177  0.000 ***  [0.338, 0.446]
#>   Preference         ~      Crisp    0.197  0.029   6.880  0.000 ***  [0.141, 0.253]
#> ────────────────────────────────────────────────────────────────────────────────────
#> *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1
#> You can drag and resize the R console to view the entire table