| dpriors {blavaan} | R Documentation |
Specify Default Prior Distributions
Description
Specify "default" prior distributions for classes of model parameters.
Usage
dpriors(..., target = "stan")
Arguments
... |
Parameter names paired with desired priors (see example below). |
target |
Are the priors for jags, stan (default), or stanclassic? |
Details
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).
Value
A character vector containing the prior distribution for each type of parameter.
References
Edgar C. Merkle, Ellen Fitzsimmons, James Uanhoro, & Ben Goodrich (2021). Efficient Bayesian Structural Equation Modeling in Stan. Journal of Statistical Software, 100(6), 1-22. URL http://www.jstatsoft.org/v100/i06/.
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/.
See Also
Examples
dpriors(nu = "normal(0,10)", lambda = "normal(0,1)", rho = "beta(3,3)")