BANOVA.multi.mediation {BANOVA} | R Documentation |

## Mediation analysis with multiple possibly correlated mediators

### Description

`BANOVA.multi.mediation`

is a function for analysis of multiple possibly correlated mediators.
These mediators are assumed to have no causal influence on each other.
Both single-level and multi-level models can be analyzed.

### Usage

```
BANOVA.multi.mediation(sol_1, sol_2, xvar, mediators, individual = FALSE)
```

### Arguments

`sol_1` |
an object of class "BANOVA" returned by BANOVA.run function with a fitted model for an outcome variable regressed on a causal variable, a mediator, and, possibly, moderators and control variables. The outcome variable can follow Normal, T, Poisson, Bernoulli, Binomial, Truncated Normal and ordered Multinomial distributions. |

`sol_2` |
an object of class "BANOVA" returned by BANOVA.run function, which contains an outcome of the analysis for multiple Multivariate Normal mediators regressed on a casual variable and other possible moderators and control variables. |

`xvar` |
a character string that specifies the name of the causal variable used in both models. |

`mediators` |
a vector with character strings, which specifies the names of the mediator variables used in the models. |

`individual` |
logical indicator of whether to output effects for individual units in the
analysis (TRUE or FALSE). This analysis requires a multilevel |

### Details

The function extends `BANOVA.mediation`

to the case with multiple possibly
correlated mediators. For details about mediation analysis performed in BANOVA see
the help page for the BANOVA.mediation.

`BANOVA.multi.mediation`

estimates and tests specific indirect effects of the causal
variable conveyed through each mediator. Furthermore, the total indirect effect of the causal
variables are computed as a sum of the specific indirect effects.

The function prints multiple tables with mediated effects. Tables with direct effects of the causal variable and mediators on the outcome variable, as well as direct effects of the causal variable on the mediators include a posterior mean and 95% credible intervals of the effects. Next, the function displays on the console tables with specific indirect effects and effect sizes of the mediators, followed by the TIE of the causal variable. These tables include the mean, 95% credible intervals, and two-sided Bayesian p-values.

### Value

Returns an object of class `"BANOVA.multi.mediation"`

. The returned object is a list
containing:

`dir_effects` |
table or tables with the direct effect. |

`individual_direct` |
is returned if |

`m1_effects` |
a list with tables of the effects of the mediator on the outcome |

`m2_effects` |
a list with tables of the effect of the causal variable on the mediator |

`indir_effects` |
tables of the indirect effect |

`individual_indirect` |
is returned if |

`effect_sizes` |
a list with effect sizes on individual mediators |

`total_indir_effects` |
table or tables with the total indirect effect of the causal variable |

`xvar` |
the name of the causal variable |

`mediators` |
the names of the mediating variables |

`individual` |
the value of the argument individual (TRUE or FALSE) |

### Author(s)

Anna Kopyakova

### Examples

```
# Use the colorad data set
data(colorad)
# Add a second mediator to the data set
colorad$blur_squared <- (colorad$blur)^2
# Prepare mediators to be analyzed in the Multivariate Normal model
mediators <- cbind(colorad$blur, colorad$blur_squared)
colnames(mediators) <- c("blur", "blur_squared")
colorad$mediators <- mediators
# Build and analyze the model for the outcome variable
model <- BANOVA.model('Binomial')
banova_binom_model <- BANOVA.build(model)
res_1 <- BANOVA.run(y ~ typic, ~ color + blur + blur_squared, fit = banova_binom_model,
data = colorad, id = 'id', num_trials = as.integer(16),
iter = 2000, thin = 1, chains = 2)
# Build and analyze the model for the mediators
model <- BANOVA.model('multiNormal')
banova_multi_norm_model <- BANOVA.build(model)
res_2 <- BANOVA.run(mediators ~ typic, ~ color, fit = banova_multi_norm_model,
data = colorad, id = 'id', iter = 2000, thin = 1, chains = 2)
# Calculate (moderated) effects of "typic" mediated by "blur" and "blur_squared"
results <- BANOVA.multi.mediation(res_1, res_2, xvar='typic', mediators=c("blur", "blur_squared"))
```

*BANOVA*version 1.2.1 Index]