Specify default prior distributions
Specify "default" prior distributions for classes of model parameters.
dpriors(..., target = "stan")
... |
Parameter names paired with desired priors (see example below). |
target |
Are the priors for jags, stan (default), or stanclassic? |
The prior distributions always use JAGS/Stan syntax and parameterizations. For example, the normal distribution in JAGS is parameterized via the precision, whereas the normal distribution in Stan is parameterized via the standard deviation.
User-specified prior distributions for specific parameters
(using the prior() operator within the model syntax) always
override prior distributions set using dpriors().
The parameter names are:
nu: Observed variable intercept parameters.
alpha: Latent variable intercept parameters.
lambda: Loading parameters.
beta: Regression parameters.
itheta: Observed variable precision parameters.
ipsi: Latent variable precision parameters.
rho: Correlation parameters (associated with covariance parameters).
ibpsi: Inverse covariance matrix of
blocks of latent variables (used for target="jags").
tau: Threshold parameters (ordinal data only).
delta: Delta parameters (ordinal data only).
A character vector containing the prior distribution for each type of parameter.
Edgar C. Merkle & Yves Rosseel (2018). blavaan: Bayesian Structural Equation Models via Parameter Expansion. Journal of Statistical Software, 85(4), 1-30. URL http://www.jstatsoft.org/v85/i04/.
dpriors(nu = "normal(0,10)", lambda = "normal(0,1)", rho = "beta(3,3)")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.