bmlm (version 1.3.15)

mlm: Estimate a multilevel mediation model

Description

Estimates a Bayesian multilevel mediation model using Stan.

Usage

mlm(
  d = NULL,
  id = "id",
  x = "x",
  m = "m",
  y = "y",
  priors = NULL,
  binary_y = FALSE,
  ...
)

Value

An object of S4 class stanfit, with all its available methods.

Arguments

d

A data.frame or a data_frame.

id

Column of participant IDs in data.

x

Column of X values in data.

m

Column of M values in data.

y

Column of Y values in data.

priors

A list of named values to be used as the prior scale parameters. See details.

binary_y

Set to TRUE if y is binary and should be modelled with logistic regression. Defaults to FALSE (y treated as continuous.) This feature is experimental.

...

Other optional parameters passed to rstan::stan().

Author

Matti Vuorre

Details

Draw samples from the joint posterior distribution of a multilevel mediation model using Stan.

Priors

Users may pass a list of named values for the priors argument. The values will be used to define the scale parameter of the respective prior distributions. This list may specify some or all of the following parameters:

dy, dm

Regression intercepts (for Y and M as outcomes, respectively.)

a, b, cp

Regression slopes.

tau_x

Varying effects SDs for above parameters (e.g replace x with a.)

lkj_shape

Shape parameter for the LKJ prior.

See examples for specifying the following: Gaussian distributions with SD = 10 as priors for the intercepts, Gaussians with SD = 2 for the slopes, Half-Cauchy distributions with scale parameters 1 for the varying effects SDs, and an LKJ prior of 2.

Examples

if (FALSE) {
## Run example from Bolger and Laurenceau (2013)
data(BLch9)
fit <- mlm(BLch9)
mlm_summary(fit)

### With priors
Priors <- list(dy = 10, dm = 10, a = 2, b = 2, cp = 2,
               tau_dy = 1, tau_dm = 1, tau_a = 1, tau_b = 1, tau_cp = 1,
               lkj_shape = 2)
fit <- mlm(BLch9, priors = Priors)
}