mlm {bmlm} | R Documentation |
Estimates a Bayesian multilevel mediation model using Stan.
mlm(d = NULL, id = "id", x = "x", m = "m", y = "y", priors = NULL, binary_y = FALSE, ...)
d |
A |
id |
Column of participant IDs in |
x |
Column of X values in |
m |
Column of M values in |
y |
Column of Y values in |
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 |
Draw samples from the joint posterior distribution of a multilevel mediation model using Stan.
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:
Regression intercepts (for Y and M as outcomes, respectively.)
Regression slopes.
Varying effects SDs for above parameters (e.g replace x with a.)
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.
An object of S4 class stanfit, with all its available methods.
Matti Vuorre mv2521@columbia.edu
## Not run: ## 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) ## End(Not run)