Specify "default" prior distributions for classes of model parameters.
dpriors(..., target = "stan")A character vector containing the prior distribution for each type of parameter.
Parameter names paired with desired priors (see example below).
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).
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/.
bcfa, bsem, bgrowth
dpriors(nu = "normal(0,10)", lambda = "normal(0,1)", rho = "beta(3,3)")
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